prospective cohort study research design

Cohort Studies: Design, Analysis, and Reporting

Affiliations.

• 1 Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH. Electronic address: [email protected].
• 2 Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH.
• PMID: 32658655
• DOI: 10.1016/j.chest.2020.03.014

Cohort studies are types of observational studies in which a cohort, or a group of individuals sharing some characteristic, are followed up over time, and outcomes are measured at one or more time points. Cohort studies can be classified as prospective or retrospective studies, and they have several advantages and disadvantages. This article reviews the essential characteristics of cohort studies and includes recommendations on the design, statistical analysis, and reporting of cohort studies in respiratory and critical care medicine. Tools are provided for researchers and reviewers.

Keywords: bias; cohort studies; confounding; prospective; retrospective.

Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Publication types

• Cohort Studies*
• Data Interpretation, Statistical
• Guidelines as Topic
• Research Design / statistics & numerical data*

• En español – ExME
• Em português – EME

Cohort studies: prospective and retrospective designs

Posted on 6th March 2019 by Izabel de Oliveira

In epidemiology, the term “cohort” is used to define a set of people followed for a certain period of time. W. H. Frost, a 20th century epidemiologist, was the first to adopt the term in a 1935 publication, when he assessed age-specific and tuberculosis-specific mortality rates. The epidemiological definition of the word currently means:

a group of people with certain characteristics, followed up in order to determine incidence or mortality by any specific disease, all causes of death or some other outcome. [1]

Cohort study design is described as ‘observational’ because, unlike clinical studies, there is no intervention. [2] Because exposure is identified before outcome , cohort studies are considered to provide stronger scientific evidence than other observational studies such as case-control studies. [1] A fundamental characteristic of the study is that at the starting point, subjects are identified and exposure to particular risk factors is assessed. Subsequently, the frequency of the outcome, usually the incidence of disease or death over a period of time, is measured and related to exposure status. [3]

Relative risk (RR) is the measure of association that is applied for the analysis of the results in cohort studies. It compares the incidence of the disease in the exposed group with the incidence in the non-exposed group, hence the name relative risk or risk ratio. If the incidence in the two groups is equal, the value for the RR will be 1, but if the value is greater than 1, this indicates a positive relationship between the risk factor and the outcome. In order to determine if the sample studied reflects a real effect of the risk factor in the population, the sample variability of the findings may be evaluated through tests of significance or confidence intervals. [4]

Advantages of cohort studies include the possibility of examining multiple results from a given exposure, determining disease rates in exposed and unexposed individuals over time, and investigating multiple exposures. In addition, cohort studies are less susceptible to selection bias than case-control studies. The disadvantages are the weaknesses of observational design, the inefficiency to study rare diseases or those with long periods of latency, high costs, time consuming, and the loss of participants throughout the follow-up which may compromise the validity of the results. [5]

Prospective Cohort Studies

Prospective cohort studies are characterised by the selection of the cohort and the measurement of risk factors or exposures before the outcome occurs, thus establishing temporality, an important factor in determining causality. This design provides a different advantage over case-control studies in which exposure and disease are assessed at the same time. [6]

The study is carried out in three fundamental stages: identification of the individuals, observation of each group over time to evaluate the development of the disease in the groups, and comparison of the risk of onset of the disease between exposed and non-exposed groups. [5]

The main disadvantage to prospective cohort studies is the cost. It requires a large number of individuals to be followed up for long periods of time [6] and this can be difficult due to loss to follow-up or withdrawal by the individuals studied. [1] Biases may occur, especially if there is significant loss during follow-up. [6]

It is important to minimise loss to follow-up , a situation in which the researcher loses contact with the individual, resulting in missing data. When loss to follow-up of many individuals occurs, the internal validity of the study is reduced. As a general rule, the loss rate should not exceed 20% of the sample. Any systematic differences related to the outcome or exposure of risk factors for those who drop out and those who remain in the study should be examined, if possible. Strategies to avoid loss to follow-up are to exclude individuals who are likely to be lost, such as those who plan to move, and to obtain information to enable future tracking and to maintain periodic contact. [1]

Prospective design is inefficient and inappropriate for the study of rare diseases, but it becomes more efficient when there is an increase in the frequency of the disease in the population. [6]

The Nurses’ Health Study…

The Nurses’ Health Study (NHS) [7] is among the largest prospective investigation into the risk factors for major chronic diseases in women. Donna Shalala, former Secretary of the U.S. Department of Health and Human Services, called the NHS “one of the most significant studies ever conducted on the health of women.”

The Nurses’ Health Study (NHS) was established by Dr. Frank Speizer in 1976 with continued funding from the National Institutes of Health since then. The primary motivation for the study was to investigate the potential long-term consequences of oral contraceptives which were being prescribed to millions of women.

Nurses were selected as the study population because of their knowledge about health and their ability to provide complete and accurate information regarding various diseases due to their nursing education. They were relatively easy to follow over time and were motivated to participate in a long-term study. The cohort was limited to married women due to the sensitivity of questions about contraceptive use at that time.

The original focus of the study was on contraceptive methods, smoking, cancer, and heart disease, but has expanded over time to include research on many other lifestyle factors, behaviours, personal characteristics, and also other diseases.

Retrospective Cohort Studies

Cohort studies can also be retrospective. Retrospective cohorts are also called historical cohorts. [1,8] A retrospective cohort study considers events that have already occurred. Health records of a certain group of patients would already have been collected and stored in a database, so it is possible to identify a group of patients – the cohort – and reconstruct their experience as if it had been prospectively followed up. [2]

Although patient information was probably collected prospectively, the cohort would not have initially identified the goal of following individuals and investigating the association between risk factor and outcome. In a retrospective study, it is likely that not all relevant risk factors have been recorded. This may affect the validity of a reported association between risk factor and outcome when adjusted for confounding . In addition, it is possible that the measurement of risk factors and outcomes would not have been as accurate as in a prospective cohort study. [2]

Many of the advantages and disadvantages of retrospective cohort studies are similar to those of prospective studies. As previously described, retrospective cohort studies are typically constructed from previously collected records, in contrast to prospective design, which involves identification of a prospectively followed group, with the objective of investigating the association between one or more risk factors and outcome. However, an advantage to both study designs is that exposure to risk factors can be recorded before the outcome occurs. This is important because it allows the sequence of risk and outcome factors to be evaluated. [8]

Use of previously collected and stored records in a database indicates that the retrospective cohort study is relatively inexpensive and quick and easy to perform. However, with retrospective cohorts, it is possible that not all relevant risk factors have been identified and recorded. Another disadvantage is that many health professionals will have become involved in patient care, making the measurement of risk factors and outcomes less consistent than that achieved with a prospective study design. [8]

Dying to be famous…

Rock and pop fame is associated with risk taking, substance use and premature mortality. This retrospective cohort study [9] examined the relationships between fame and premature mortality and tested how these relationships vary with the type of performer (solo or band member) and nationality and whether the cause of death was linked to adverse childhood experiences.

The cohort included 1,489 rock and pop stars that reached fame between 1956 and 2006. The study examined the risk and protective factors for star mortality, relative contributions of adverse childhood experiences and other performance characteristics to cause premature death between rock and pop stars.

Although artists are generally not accessible through search techniques, considerable information is available through biographical publications, news and other media coverage. The accuracy and completeness of the data collected from the media and biographical sources cannot be quantified. However, such limitations are unlikely to have generated the patterns identified in this study.

The study concluded that the association between fame and mortality is mainly conditioned to performers’ characteristics. Adverse experiences in their lives predisposed them to adopt health-damaging behaviours, and fame and wealth provide greater opportunities to engage in risk-taking. Young people wish to emulate their idols, so it is important they recognise that drug abuse and risk-taking may be rooted in negative experiences rather than seeing them related to success.

Take home points:

• Cohort studies are appropriate studies to evaluate associations between multiple exposures and multiple outcomes.
• An advantage of prospective and retrospective cohort designs is that they are able to examine the temporal relationship between the exposure and the outcome.

Izabel de Oliveira

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• Published: 13 January 2022

Cohort studies investigating the effects of exposures: key principles that impact the credibility of the results

• Anna Miroshnychenko 1 ,
• Dena Zeraatkar 1 , 2 ,
• Mark R. Phillips   ORCID: orcid.org/0000-0003-0923-261X 1 ,
• Sophie J. Bakri 3 ,
• Lehana Thabane   ORCID: orcid.org/0000-0003-0355-9734 1 , 4 ,
• Mohit Bhandari   ORCID: orcid.org/0000-0001-9608-4808 1 , 5 &
• Varun Chaudhary   ORCID: orcid.org/0000-0002-9988-4146 1 , 5

for the Retina Evidence Trials InterNational Alliance (R.E.T.I.N.A.) Study Group

Eye volume  36 ,  pages 905–906 ( 2022 ) Cite this article

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• Outcomes research

What are cohort studies?

Cohort studies are observational studies that follow groups of patients with different exposures forward in time and determine outcomes of interest in each exposure group or that investigate the effect of one or more participant characteristics on prognostic outcomes [ 1 ]. The focus of this editorial is on cohort studies that investigate the effects of exposures that may be associated with an increased or a decreased occurrence of the outcome of interest. Cohort studies may be prospective or retrospective in design. In prospective cohort studies, investigators enroll participants, assess exposure status, initiate follow up, and measure the outcome of interest in the future. In retrospective cohort studies, data on both the exposures and outcome of interest have been previously collected.

Purpose of cohort studies

While large well-designed randomized controlled trials (RCTs) represent the optimal design for making inferences about the effects of exposures or interventions on health outcomes, they are often not feasible to conduct—due to costs or challenges of recruiting patients with rare conditions and following patients for sufficient durations. Further, patients included in RCTs may not be representative of patients encountered in practice and the effectiveness of therapies in strict clinical trials may be different than when implemented in routine practice. In such circumstances, well-designed observational studies, which include cohort studies, can play an important role in producing evidence to guide clinical care decisions in ophthalmology. Cohort studies can also be conducted to generate hypotheses and establishing questions for future RCTs.

The differentiating characteristics between observational (e.g., cohort study) and experimental (e.g., RCT) study designs are that in the former the investigator does not intervene and rather “observes” and examines the relationship or association between an exposure and outcome. Examples of cohort studies in ophthalmology include evaluation of a possible association between exposure to ambient air pollution and age-related cataract [ 2 ]; or assessment of the impact of eye preserving therapies for patients with advanced retinoblastoma [ 3 ].

Key determinants of credibility (i.e., internal validity) in cohort studies

Readers considering applying evidence from cohort studies should be mindful of the following factors that affect the credibility or internal validity of cohort studies.

Factors that decrease the credibility of cohort studies

Cohort studies are at serious risk of confounding bias and so adjusting or accounting for confounding factors is a priority in these studies. Confounding occurs when the exposure of interest is associated with another factor that also influences the outcome of interest. Investigators can use various design (e.g., matching) and statistical methods (e.g., adjusted analyses based on regression methods) to deal with known, measured confounders. Readers should assess whether the authors accounted for known confounders of the relationship under investigation in either their design or statistical analysis. Readers should be mindful, however, that possibility of residual confounding caused by unknown or unmeasured confounders always remains.

Inappropriate selection of participants into the cohort study can result in selection bias. Selection bias occurs when selection of participants is related to both the intervention and outcome. Bias in measurement of exposure/outcome, or detection bias, can arise when outcome assessors are aware of intervention status, different methods are used to assess outcomes in the different intervention groups, and/or the exposure status is misclassified differentially or non-differentially (i.e., the probability of individuals being misclassified is different or equal between groups in a study, respectively).

Missing data may also affect the credibility of cohort studies. Bias due to missing data in prospective and retrospective studies arises when follow up data are missing for individuals initially included in the study. Participants with missing outcome data may differ importantly from those with complete data (e.g., they may be healthier or may not have experienced adverse events).

Last, credibility of a cohort study may be affected by the reporting of results. Selective reporting arises when investigators selectively report results in studies in such a way so that the study report highlights or emphasizes evidence supporting a particular hypothesis and does not report or understates evidence supporting an alternative hypothesis. Investigators may selectively report results for timepoints or measures that produced results consistent with their preconceived beliefs or results that were newsworthy and disregard results for timepoints or measures that produced results that were inconsistent with their beliefs or considered not newsworthy. Publication bias refers to the propensity for studies with anomalous, interesting, or statistically significant results to be published at higher rates or to be published more rapidly or to be published in journals with higher visibility.

Factors that increase the credibility of cohort studies

Three uncommon situations can sometimes make us more certain of findings of cohort studies—in some circumstances, these situations can make us as confident of evidence from cohort studies as we would be for evidence from a rigorous RCT. First, when the observed effect is large (typically a relative risk (RR) > 2 or RR < 0.5), biases, such as confounding, are less likely to completely explain the observed effect. Second, we may be more certain of results when we observe a dose-response gradient: biases in non-randomized studies (e.g., confounding and errors in the classification of the exposure) are unlikely to produce spurious dose-response associations., when all suspected biases are believed to act against the observed direction of effect, we can be more certain that the observed effect is not due to the suspected biases. It is, however, difficult to anticipate with sufficient certainty the direction in which effects are likely biased in complex epidemiological studies. Because situations that make us more certain of findings of cohort studies occur infrequently, cohort studies usually provide only low to very low certainty evidence [ 4 ].

Applicability (i.e., external validity) in cohort studies

If the populations, exposures, or outcomes investigated in cohort studies differ from the those of interest in routine or typical settings, the evidence may not be applicable or externally valid. Such judgements depend on whether differences between studies and the question of interest would lead to an appreciable change in the direction or magnitude of effect. Generally, observational studies (e.g., cohort studies) have higher external validity than experimental studies (e.g., RCTs) [ 5 ].

Cohort studies follow a population exposed or not exposed to a potential causal agent forward in time and assess outcomes. Cohort studies are beneficial because these studies allow the investigators to observe a possible association between an exposure and outcome of interest in a population that cannot be randomly subjected to an exposure due to ethical, methodological, or feasibility limitations. Cohort studies, however, have several limitations that should be acknowledged and minimized if possible.

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Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada

Anna Miroshnychenko, Dena Zeraatkar, Mark R. Phillips, Lehana Thabane, Mohit Bhandari, Varun Chaudhary & Lehana Thabane

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

Dena Zeraatkar

Department of Ophthalmology, Mayo Clinic, Rochester, MN, USA

Sophie J. Bakri

Biostatistics Unit, St. Joseph’s Healthcare-Hamilton, Hamilton, ON, Canada

Lehana Thabane & Lehana Thabane

Department of Surgery, McMaster University, Hamilton, ON, Canada

Mohit Bhandari, Varun Chaudhary, Varun Chaudhary & Mohit Bhandari

Retina Consultants of Texas (Retina Consultants of America), Houston, TX, USA

Charles C. Wykoff

Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, USA

NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK

Sobha Sivaprasad

Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA

Peter Kaiser

Retinal Disorders and Ophthalmic Genetics, Stein Eye Institute, University of California, Los Angeles, CA, USA

David Sarraf

The Retina Service at Wills Eye Hospital, Philadelphia, PA, USA

Sunir J. Garg

Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA

Rishi P. Singh

Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA

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Singapore Eye Research Institute, Singapore, Singapore

Tien Y. Wong

Singapore National Eye Centre, Duke-NUD Medical School, Singapore, Singapore

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Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, VIC, Australia

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AM was responsible for writing, critical review and feedback on manuscript. DZ was responsible for writing, critical review and feedback on manuscript. MRP was responsible for conception of idea, critical review and feedback on manuscript. SJB was responsible for critical review and feedback on manuscript. LT was responsible for critical review and feedback on manuscript. MB was responsible for conception of idea, critical review and feedback on manuscript. VC was responsible for conception of idea, critical review and feedback on manuscript.

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Miroshnychenko, A., Zeraatkar, D., Phillips, M.R. et al. Cohort studies investigating the effects of exposures: key principles that impact the credibility of the results. Eye 36 , 905–906 (2022). https://doi.org/10.1038/s41433-021-01897-0

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• What Is a Cohort Study? | Definition & Examples

What Is a Cohort Study? | Definition & Examples

Published on February 24, 2023 by Tegan George .

A cohort study is a type of observational study that follows a group of participants over a period of time, examining how certain factors (like exposure to a given risk factor) affect their health outcomes. The individuals in the cohort have a characteristic or lived experience in common, such as birth year or geographic area.

While there are several types of cohort study—including open, closed, and dynamic—there are two that are particularly common: prospective cohort studies and retrospective cohort studies .

The initial cohort consisted of about 18,000 newborns. They were enrolled in the study shortly after birth, with regular follow-ups, medical examinations, and cognitive assessments to track their physical, social, and cognitive development.

Cohort studies are particularly useful for identifying risk factors for diseases. They can help researchers identify potential interventions to help prevent or treat the disease, and are often used in fields like medicine or healthcare research.

Table of contents

When to use a cohort study, examples of cohort studies, advantages and disadvantages of cohort studies, frequently asked questions.

Cohort studies are a type of observational study that can be qualitative or quantitative in nature. They can be used to conduct both exploratory research and explanatory research depending on the research topic.

In prospective cohort studies , data is collected over time to compare the occurrence of the outcome of interest in those who were exposed to the risk factor and those who were not. This can help ascertain whether the risk factor could be associated with the outcome.

In retrospective cohort studies , your participants must already possess the disease or health outcome being studied prior to joining. The study is then focused on analyzing the health outcomes of those who share the exposure to the risk factor over a period of time.

A cohort study could be a good fit for your research if:

• You have access to a large pool of research subjects and are comfortable and able to fund research stretching over a longer timeline.
• The relationship between the exposure and health outcome you’re studying is not well understood, and/or its long-term effects have not been thoroughly investigated.
• The exposure you’re studying is rare, or there are possible ethical considerations preventing you from a traditional experimental design .
• Cohort studies in general are more longitudinal in nature. They usually follow the group studied over a long period of time, investigating how certain factors affect their health outcomes.
• Case–control studies rely on primary research , comparing a group of participants already possessing a condition of interest to a control group lacking that condition in real time.

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Cohort studies are common in fields like medicine, epidemiology, and healthcare.

Cohort studies are a strong research method , particularly in epidemiology, health, and medicine, but they are not without their disadvantages.

Advantages of cohort studies

Advantages of cohort studies include:

• Cohort studies are better able to approach an estimation of causality than other types of observational studies. Due to their ability to establish temporality, multiple outcomes, and disease incidence over time, researchers are able to determine with more certainty that the exposure indeed preceded the outcome. This strengthens a claim for a cause-and-effect relationship between the variables of interest.
• Due to their long nature, cohort studies are a particularly good choice for studying rare exposures , such as exposure to a new drug or an environmental toxin. Other research designs aren’t able to incorporate the breadth and depth of the impact as broadly as cohort studies do.
• Because cohort studies usually rely on large groups of participants, they are better able to control for potentially confounding variables , such as age, gender identity, or socioeconomic status. Relatedly, the ability to use a sampling method that ensures a more representative sample of the population leads to findings that are typically much more generalizable , with higher internal validity and external validity .

Disadvantages of cohort studies

Disadvantages of cohort studies include:

• Cohort studies can be extremely time-consuming and expensive to conduct due to their long and intense nature.
• Cohort studies are at risk for biases inherent to long-term studies like attrition bias and survivorship bias , as participants are likely to drop out over time. Measurement errors like omitted variable bias and information bias can also confound your analysis, leading you to draw conclusions that may not be true.
• Like many other experimental designs , cohort studies can raise questions regarding ethical considerations . This is particularly the case if the exposure of interest is harmful, or if there is no known treatment for it. Prior to beginning your research, it is critical to ensure that participation in your study is fully voluntary, informed, and as safe as it can be for your research subjects.

The easiest way to remember the difference between prospective and retrospective cohort studies is timing. 

• A prospective cohort study moves forward in time, following a group of participants to track the development of an outcome of interest.
• A retrospective cohort study moves backward in time, first identifying a group of people who already possess the outcome of interest, and then looking backwards to assess their exposure to a risk factor.

A closed cohort study is a type of cohort study where all participants are selected at the beginning of the study, with no new participants added during any of the follow-up periods.

This approach is useful when the exposure being studied is rare, or when it isn’t practically or financially feasible to recruit new participants.

In a cohort study , the incidence refers to the number of new cases of a disease or health outcome that develop during the study period, while prevalence refers to the proportion of the population who have the disease or health outcome at a given point in time. Cohort studies are particularly useful for measuring incidence rates.

A dynamic cohort study is a type of cohort study where the participants are not fixed at the start of the study. Instead, new participants can be added over time if they become eligible to participate. This approach is useful when the study population is expected to change over time.

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George, T. (2023, February 24). What Is a Cohort Study? | Definition & Examples. Scribbr. Retrieved April 15, 2024, from https://www.scribbr.com/methodology/cohort-study/

Euser, A. M., Zoccali, C., Jager, K. J., & Dekker, F. W. (2009). Cohort Studies: Prospective versus Retrospective. Nephron Clinical Practice , 113 (3), c214–c217. https://doi.org/10.1159/000235241

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Prospective Cohort Studies in Medical Research

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DOI: 10.5772/intechopen.76514

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Cohort studies are the analytical design of observational studies that are epidemiologically used to identify and quantify the relationship between exposure and outcome. Due to the longitudinal design, cohort studies have several advantages over other types of observational studies. The purpose of this chapter is to cover the various characteristics of prospective cohort studies. This chapter is divided into three main sections. In the first we introduce the concept and ranking of cohort studies, as well as the advantages and disadvantages. In the second we focus on the design of cohort studies, mainly its prospective aspect, and the distinguishing features from the retrospective type. The section also covers the essential characteristics of a cohort study design and its varied applications in medical research. In the third we go over examples of prospective studies in the medical field. For each, an overview of the study design is given, along with a random selection of study findings/impact, strengths and weaknesses.

• observational study
• cohort study
• prospective cohort study
• longitudinal study
• study design
• epidemiology
• medical research

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Samer hammoudeh.

• Hamad Medical Corporation, Doha, Qatar

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Ibrahim janahi *.

*Address all correspondence to: [email protected]

1. Cohort studies

1.1. introduction.

The term “cohort” originates from Latin “cohors” [ 1 ]. A term that was used in the military back in Roman times, which referred to a unit that is comprised of 300–600 men, of which each 10 cohorts were named a legion [ 2 ]. In the field of epidemiology, Frost was the first to introduce the term “cohort study” back in 1935 [ 3 ]. Cohort refers to a group of individuals that share a common factor or a defining characteristic [ 4 , 5 ], or in other words, cohort is a certain component of a specific population that can be measured and followed throughout time [ 6 ]. Cohort studies are classified under the non-experimental type of studies [ 4 ], which are observational by default [ 7 ].

A cohort study follows people as groups, two or more, from exposure to outcome [ 2 , 8 ]. The two groups would be categorized based on their exposure status to “exposed” and “unexposed” [ 4 , 9 , 10 ]. If there were multiple groups then these would be categorized either by the type or level of exposure [ 4 ]. The main characteristic of a cohort study is that it follows participants in a forward manner, from the presence of the exposure to the presence of the outcome [ 2 , 9 , 10 , 11 ]. Or as De Rango describes it: using a longitudinal pattern, a cohort study, follows a group or groups of individuals over time in order to ascertain the incidence of a predetermined outcome after being exposed to a certain factor, whether being a risk factor, medication, or intervention [ 12 ]. Cohort studies can either be prospective (concurrent) or retrospective (non-concurrent) [ 9 ].

1.2. Ranking of cohort studies

Researchers agree that cohort studies, as related to the hierarchy of evidence, rank below meta-analysis, systematic review and randomized controlled trial, but rank higher than case–control studies, cross sectional studies, case series/reports [ 13 , 14 , 15 , 16 ]. As newer models or classifications of the hierarchy of evidence have emerged, where meta-analysis and systematic reviews have been removed from the hierarchy and repositioned as a magnifying glass or a lens through which evidence from other types of studies can be viewed or scrutinized; cohort studies remain below randomized controlled trials and higher than the other types [ 17 ]. Cohort studies provide information on the relationship between exposure and outcome when a randomized controlled trial is not possible to conduct for whatever reason [ 6 , 15 ].

1.3. Advantages of cohort studies

Cohort studies are the design of choice when randomization is not practical or ethical [ 6 , 18 ]. They are also useful in the study of infections [ 9 ] and for hypothesis generation [ 19 ]. Due to the design of cohort studies, and since temporal sequence is present, both incidence rate and cumulative incidence can be calculated [ 2 , 8 , 20 , 21 , 22 ]. They also allow for the measurement of relative risk (RR) [ 2 , 8 , 23 ], hazard ratio [ 8 ], and attributable risk [ 8 , 23 ]. Furthermore, they allow for the study of multiple outcomes that can be associated with a single type of exposure [ 2 , 20 ] or multiple exposures [ 18 ]. Additionally, they allow for the study of rare exposures [ 2 , 18 , 20 ]. Finally, cohort studies have lower risk of encountering survivor bias [ 2 ], and recall bias [ 9 , 21 ]. Survivor bias occurs when focusing only on those who survived or made it through a certain criteria or point, and ignoring those that didn’t, such as studying rapidly fatal diseases [ 2 ].

1.4. Disadvantages of cohort studies

Among the disadvantages of cohort studies is selection bias, which may occur when the participants are not representative of the population or of the patient grouping that they fall under. This in turn will influence how well or not the results can be generalized to the rest of the population, in what is known as external validity [ 2 , 12 , 18 , 24 , 25 ]. This will be covered later in section three of this chapter under aspects of cohort studies. Another disadvantage is that causation cannot be established from cohort studies [ 18 , 20 ], as it would require an experimental design in order to determine any causal effect [ 20 ]. However, due to the longitudinal design of cohort studies, they may aid in studying a certain causal hypothesis [ 20 ]. A third disadvantage is that they require a large sample size, which might pose an issue when dealing with outcomes that take a long time to develop [ 10 ]. Finally, cohort studies cannot be used to study rare outcomes [ 23 ].

2. Prospective cohort studies

2.1. types of cohort studies.

Cohort studies are either prospective or retrospective [ 1 , 2 , 18 ]. In the former, the researcher would assess exposure at baseline and then follow the person over time in order to determine the outcome such as the development of a disease [ 9 , 18 , 20 , 21 , 26 ]. In the latter, the order is reversed, as a cohort is established after the follow up has been conducted, or the outcome has developed, and exposure is then assessed in a retrospective manner [ 9 , 18 , 20 , 21 , 27 ]. Merrill indicates that the outcome status at the start of the study is what determines the overall study type. If the outcome has not yet developed then it is a prospective study, and if the outcome has already developed then it is a retrospective study [ 23 ]. Cohort studies can also be classified based on whether or not participants are replaced once they are lost. If those that drop out or are lost to follow up are replaced with new participants, then this would be classified as a dynamic or an open cohort. In the case that those lost do not get replaced, then it would be classified as a fixed or closed cohort [ 4 , 20 ].

2.1.1. Prospective cohort studies

Prospective cohort studies, as the name indicates, observes a group of people after being exposed to a certain factor in order to investigate the outcome, following the natural sequence of time, starting with the present and looking forward in time [ 12 , 18 , 20 ], which in turn provides true risk (absolute) estimates for the groups under investigation [ 26 ]. It is considered the gold standard among observational studies [ 8 ]. Under this type of study, the researcher would have control over data collection methodology, as well as the overall cohort study set up, which gives prospective cohort studies an advantage over retrospective cohort studies [ 9 ]. Further advantages and disadvantages of prospective cohort studies are discussed below.

2.1.1.1. Advantages of prospective cohort studies

Euser et al. highlight the major advantage of prospective cohort studies as being accurate in regards to the information collected about exposures, endpoints, and confounders [ 18 ]. Others list the following as advantages of prospective cohort studies; first: the exposure has already been measured before the outcome has occurred, which allows for the assessment of temporal sequence [ 28 ]. This allows for the calculation of incidence and the determination of the disease process [ 2 , 12 , 20 , 23 ]. Second: elimination of recall bias, as there is no need for any recollection of information since the data is being collected in a prospective manner [ 7 ]. However, Kip et al. reported that recall bias can pose an issue in prospective cohort studies if the exposure is self-reported, brief, and requires multiple measurements, such as stress episodes [ 29 ]. Third: It allows for the study of exposures were randomization is not practical or ethical [ 12 ]. Fourth: it allows for the study of rare exposures [ 20 ]. Fifth: it allows for the study of multiple outcomes [ 20 , 26 ].

2.1.1.2. Disadvantages of prospective cohort studies

Among the disadvantages of prospective cohort studies is the loss to follow up, which is common among cohort studies. This can ultimately lead to differential loss to follow up among those exposed and unexposed, which in turn can complicate the interpretation of the results [ 2 , 7 , 12 , 18 , 24 ]. Another disadvantage is that they are time consuming if follow up periods are far apart. This would be resource consuming as well, which would make prospective cohort studies not suitable for the study of outcomes that take long time to develop [ 18 , 20 , 24 , 26 ]. A third disadvantage is that they are expensive to conduct [ 18 , 20 , 30 ]. The third section of this chapter is dedicated to providing examples of prospective cohort studies.

2.1.2. Retrospective cohort studies

As previously described, retrospective cohort studies, also known as historic [ 28 ] or historical [ 24 ] cohorts, use data that has already been collected, such as databases of healthcare records, in order to investigate the association between the exposure and the outcome [ 22 , 24 , 26 , 28 ]. Although the outcome has already occurred, the design of retrospective cohort studies is similar to those of prospective cohort studies [ 22 ]. They also have similar advantages and disadvantages [ 26 , 28 ]. Hess indicates that retrospective studies in general are useful as pilot studies for future prospective studies [ 31 ].

Retrospective cohort studies have advantages and disadvantages. They are time efficient and cheap since the data has been collected previously and is available for scrutiny [ 18 , 20 , 26 ]. Additionally, since the exposure has already been measured before the outcome has occurred, this allows for the assessment of temporal sequence [ 28 ]. However, retrospective cohort studies use information that has been collected in the past for another objective other than the current study [ 18 ], and in some cases, collected for a purpose that is not related to medical research [ 9 ]. Due to this factor, the investigator lacks control over the collection of data [ 24 , 26 , 27 ]. Additionally, the measurement of exposure and outcome might be inconsistent or inaccurate, which can become a source of bias [ 24 , 27 , 28 , 31 , 32 ].

High plasma phosphate as a risk factor for decline in renal function and mortality in pre-dialysis patients [ 18 , 33 ]. In this study, Voormolen et al. followed the clinical course among incident pre-dialysis patients, using medical charts, to study the decline in kidney function and its association with plasma phosphate levels [ 18 , 33 ].

Assessment of female sex as a risk factor in atrial fibrillation in Sweden: nationwide retrospective cohort study [ 28 , 34 ]. In this study, Friberg et al. investigated gender differences in the incidence of stroke among those with atrial fibrillation using the Swedish hospital discharge registry [ 28 , 34 ].

Outcomes of care by hospitalists, general Internists, and family physicians [ 35 ]. In this study, Lindenauer et al. collected data from various hospitals in the USA, and compared the outcome of patients treated by the three types of care provider [ 35 ].

2.1.3. Aspects of cohort studies

2.1.3.1. validity.

Validity is the epidemiological assessment to the lack of systematic error [ 4 , 11 ]. There are two types of validity: internal validity and external validity [ 4 , 11 , 25 ]. Internal validity refers to the inferences made from the study that are related to the same source population [ 4 , 5 , 11 , 25 , 36 ], as to whether or not the study has measured what it had originally planned on measuring [ 25 , 36 ]. For an example, if the exposure caused the observed change in the outcome, then the study would be considered to have high internal validity [ 11 ]. On the other hand, if the observed change in the outcome was caused by a systematic error (bias), then the study would be considered to have low internal validity [ 11 ]. Threats or violations to internal validity will be discussed later in this section under bias.

External validity refers to the degree to which the study results can be generalized to other populations [ 4 , 5 , 11 , 25 , 36 ]. For example, if the study participants were not representative of the general population, then the study results cannot be generalizable to others [ 12 ]. The highest level of external validity occurs when the results can be generalized to three other domains: other populations, other environments, and other times [ 36 ]. External validity can be improved by using random selection [ 37 ].

It is essential to have internal validity in order to establish external validity; that is the study must have internal validity in the first place in order to have external validity [ 4 , 11 ]. For an example, if the exposure caused the observed change in the outcome, then the results can be generalizable to others. If the observed change was caused by any other factor, then the results cannot be generalized to others [ 4 , 11 ]. Based on the validity hierarchy, cohort studies are considered to have low internal validity, while the external validity is high [ 11 , 16 ].

2.1.3.2. Bias

Bias is a study systematic error in the design, conduct, or analysis that can be categorized into three main categories: selection bias, information bias, and confounding [ 4 , 25 , 38 ]. Selection bias occurs when the sample chosen for the study is not obtained randomly, so that the sample chosen is no longer representative of the overall population [ 4 , 25 , 38 , 39 ]. This type of bias includes three types: attrition bias, non-respondent bias, and the healthy entrant effect [ 38 ]. Attrition bias, or loss to follow up bias, occurs due to dropouts or death, which can be encountered in studies with long follow up durations (prospective) [ 23 ]. Non-respondent bias occurs when those that respond are different than those that don’t respond. For example, nonsmokers are more likely to return questionnaires about smoking than smokers are [ 25 ]. The healthy entrant effect or the healthy worker effect occurs when there are differences between those that are exposed and those that are not exposed. For an example, when comparing working individuals to the general population, as workers are more likely to be healthier than the general population. In order to avoid this type of bias, it is recommended to use two similar groups, such as using two groups of working individuals [ 23 ].

Information bias (measurement bias) [ 25 ], occurs when the data obtained is being recorded inaccurately [ 4 , 25 , 38 , 39 , 40 ]. This type of bias can be differential (nonrandom) or nondifferential (random) as related to the outcome [ 4 , 9 , 23 , 25 ]. The former is dependent on other variables and leads to overestimation or underestimation of any possible association, while the latter is independent from other variables and leads to underestimation of any possible association [ 4 , 9 , 23 ], and if the exposure was dichotomous, this type leads to bias towards the null [ 9 ]. Non differential is more commonly encountered in cohort studies [ 9 ]. Information bias can be reduced by using standardized assessment tools that have been validated [ 9 ]. Information bias is also known as classification bias, observation bias [ 25 ], or misclassification bias [ 23 ].

Confounding: confounding is a distortion of the effect [ 4 , 25 ] that may lead to overestimation or underestimation of an effect, or even reversing the direction of an effect [ 4 ]. A confounding factor is a risk factor that is associated with the exposure and influences the outcome, however, is not related to the causation sequence [ 4 , 25 , 39 ]. Unlike selection and information bias, confounding can be controlled for prior to study initiation, or after study completion [ 25 ]. Controlling for confounding factors can be accomplished through: restriction, matching, stratification, and using multivariate techniques [ 23 , 25 , 27 ].

Restriction would involve excluding those with the confounding factor [ 23 , 25 ]. If the confounding factor is categorical, then participants that fall within that category would be excluded [ 4 ], such as if smoking was considered to be a confounding factor, then those that smoke would be excluded [ 25 ]. If the confounding factor was continuous, such as age, then a range of that variable would be used to restrict the confounding [ 4 ]. Matching would involve choosing two groups that are similar to each other as much as possible [ 23 , 25 , 41 ], such as matching by gender or age [ 39 ]. Matching can be either individual matching or frequency matching. The former involves matching on an individual participant level, while the latter refers to matching on a group level [ 4 ]. Overmatching may occur when matching is being used, which may reflect on the statistical efficiency, validity, or cost efficiency of the study [ 4 ]. After the completion of the study, and during the analysis stage, stratification can be used to control for confounding by dividing the groups into several subgroups that are based on the confounding factor [ 23 , 25 , 39 , 41 ]. Multivariate techniques are also used during the analysis stage and allow for the control of multiple factors [ 25 , 39 , 41 ].

2.1.3.3. Exposure and risk

Exposure must be determined using a clear and accurate definition [ 2 , 22 ], which in some cases may involve levels of exposure [ 2 ]. This helps in eliminating possible selection bias [ 2 ]. The challenge becomes greater when there are multiple exposure assessments over an extended period of time [ 30 ]. The validity and the cost are two important aspects that must be taken into consideration when selecting an exposure measurement tool [ 30 ].

Both groups, those exposed and those that are not exposed should be at risk of eventually developing the outcome at some stage [ 2 ]. The exclusion criteria should exclude those that are not at risk of developing the outcome [ 24 ]. For an example, a study investigating the role of antipsychotics in the development of diabetes, should exclude those with diabetes to start with, since they are not at risk [ 10 ]. This helps in eliminating possible selection bias [ 2 ].

2.1.3.4. Outcomes

Outcomes should have a clear and specific definition from the beginning of the study [ 2 , 22 ], which must be measurable as well [ 2 , 22 ]. Outcomes should also be measured in a similar manner across all participants [ 2 , 22 ]. This helps in eliminating possible information bias [ 2 ]. It is recommended to use measurement tools that have been previously validated when dealing with secondary data, and to blind those who are assessing the outcome when dealing with primary data [ 10 ].

2.1.3.5. Controls

The comparison group or controls (unexposed group) should be similar to the exposed group in all possible aspects, but differ in regards to the exposure itself [ 2 ]. Three types of controls can be used, with the first being the most preferable: internal comparisons, other external cohorts, and the general population [ 2 ].

2.1.3.6. Follow up

To avoid loss to follow up and its consequent effects on the validity of the study results; measures should be taken in order to minimize the attrition rate [ 2 , 22 , 24 , 27 , 42 ]. Some of these actions include excluding those that are at high risk of not committing to the study, providing incentives for participation, collecting personal information that would allow or facilitate future contact, and maintaining ongoing contact on regular basis during the conduction period of the study [ 2 , 23 , 24 , 27 ]. The maximum acceptable limit for loss to follow up is 20% [ 23 , 24 , 42 ].

2.1.3.7. Precision

Precision is based on the absence of random error or chance [ 4 , 11 ]. This random variation can be due to the sample itself, or how it was selected, or how it was measured [ 4 , 11 ]. Standard deviations and confidence intervals are useful in determining the precision of a study, as a large standard deviation or a wide confidence interval would indicate low precision [ 11 ]. Random error or variation can be reduced by increasing the sample size [ 4 , 27 , 43 ], improving how you sample and how you measure, in addition to using the appropriate statistical methods [ 43 ].

2.1.3.8. Analysis of data

The main statistical term or product of cohort studies is the relative risk or risk ratio [ 6 , 21 ], which represents the risk of developing the outcome among those that are exposed in relation to those that are not exposed [ 20 ]. An RR that is equivalent to 1 indicates an absence of any type of association. An RR that is greater than 1 would indicate that there is a positive correlation between the exposure and risk of developing a disease. An RR that is smaller than 1 would indicate the presence of a protective effect between the exposure and the outcome [ 12 ]. Other outcome measures include: hazard ratios, survival curves, and life-table rates [ 2 ]. Some of the common statistical analysis involving cohort studies include: analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), mixed effect regression model, and generalized estimating equation models [ 7 ].

2.1.3.9. Reporting

The reporting of prospective cohort studies should follow the STROBE guidelines [ 12 ], which also apply to other observational studies [ 41 , 44 ]. This acronym stands for: Strengthening the Reporting of Observational Studies in Epidemiology. These guidelines were designed by a group of international scholars including journal editors, epidemiologists, statisticians and researchers in order to set universal standards when reporting observational studies. It is comprised of a 22 item checklist that precisely dictates what should be reported under each section of an article [ 44 , 45 , 46 , 47 ]. Sessler and Imrey indicate that the most crucial ones are related to the study: objectives, methodology, definitions, source of data, statistical analysis, participants, and results [ 41 ]. Further information can be found at http://www.strobe-statement.org/ .

Bookwala et al. outlined three main factors that aid in evaluating prospective cohort studies in their article titled “the three-minute appraisal of a prospective cohort study”. These are related to (1) comparison groups selection; (2) the impact of confounding variables; (3) type of analytical strategy used [ 48 ]. Finally, the equator network (which is supported by the University of Oxford, UK, and aims to improve the quality and transparency of health research) provides guidelines and instructions for the reporting of various kinds of studies. These can be found at www.equator-network.org . Additional information regarding what to look for in a cohort study, as well as evaluation checklists can be found elsewhere [ 2 , 8 , 11 , 25 , 39 , 48 , 49 ]. The next section of this chapter will cover examples of famous prospective cohort studies from the medical field.

3. Examples of prospective cohort studies

3.1. the framingham heart study, 3.1.1. overview.

The Framingham heart study, initiated in 1948 by The National Heart Institute (currently the National Heart, Lung, and Blood Institute) [ 50 ], is considered to be the longest, ongoing, prospective cohort study in the history of the USA [ 51 ]. Others view it as a live model that illustrates the cohort design [ 52 ]. The study was based on the hypothesis that arteriosclerosis and hypertensive cardiovascular disease are the result of several causation factors combined rather than an individual factor [ 53 ]. Based on this, the aim of the study was to investigate the factors that contribute to the development of cardiovascular disease (CVD) by following a large cohort of individuals over a long period of time [ 50 ]. Back then in 1951, when the first article about the study was published, little was known about arteriosclerosis and hypertensive cardiovascular disease [ 53 ].

The original cohort included 5209 participants, ages 30–62 years, that were recruited at the beginning of the study in the town of Framingham, Massachusetts, USA [ 50 ]. The same cohort has been followed since initiation every two years for physical, laboratory, and lifestyle examinations [ 50 ]. The second generation, the offspring cohort, was recruited in 1971 and included 5124 participants. While 1994 witnessed the enrollment of the first Omni cohort (n = 506), in order to diversify the study population. More recently in 2002, the third generation cohort (n = 4095) was enrolled, while in 2003 the new offspring cohort (n = 103), and the second Omni group (n = 410) was enrolled [ 50 ]. The study continues to follow these cohorts every 2–6 years [ 54 ]. This multi generation, multi ethnicity, enrollment design aided significantly in the study of genetics in relation to a wide range of factors and illnesses [ 51 , 54 ].

Based on the Framingham study data, since initiation and through November 2017, a total of 3561 articles have been published so far [ 55 ]. The accumulation of knowledge that has risen from this study has shed the light on cardiovascular disease risk factors [ 50 , 51 , 56 ], by further expanding on our understanding of chronic illnesses such as diabetes, obesity, metabolic syndrome and nonalcoholic fatty liver disease [ 51 , 57 ]. Such risk factors include high blood pressure, high cholesterol levels, smoking, obesity, diabetes, and physical inactivity [ 50 , 57 ].

The study was the basis of which the Framingham risk score was built on [ 56 ]. Initially published by Wilson et al. in 1998 [ 58 ], it allows for the calculation of a 10 year risk estimate of developing coronary heart disease (CHD) based on the levels of different variables [ 56 , 58 ]. This would allow for the undertaking of preventive measures [ 56 ]. Later on in 2002, the Adult Treatment Panel of the National Cholesterol Education Program used the risk score as a foundation for its risk calculator [ 56 ].

3.1.2. Study findings

The study website ( https://www.framinghamheartstudy.org/about-fhs/research-milestones.php ) covers a long list of findings, among those; cigarette smoking was discovered to increase ones risk of developing heart disease back in 1960. In 1970, high blood pressure was discovered to increase ones risk of stroke. In 1988, the beneficial effects of HDL cholesterol were discovered. In 2002, the study found that obesity is considered a risk factor leading to heart failure. More recently in 2010 sleep apnea was linked to a higher risk of stroke [ 59 ]. More information and a full list of research milestones can be found elsewhere [ 59 ].

3.1.3. Strengths and weaknesses

In addition to what had been previously discussed regarding the benefits of the prospective design of the study, a high retention rate is among the strengths of the Framingham Heart Study as participants continue to return for their follow up visits despite the years [ 54 ]. Among the weaknesses is that the study was conducted in one population residing in one locality [ 7 ], which in turn reflects on the ability to generalize findings to other populations [ 58 ]. Another weakness is that the study cohort was not randomly selected, as investigators had to use volunteers in order to obtain the necessary sample. The final cohort ended up being more healthy when compared to the general population [ 7 , 60 ].

3.2. The Nurses’ Health Study (NHS)

3.2.1. overview.

This National Institutes of Health (NIH) funded study started in 1976 [ 61 ], and as of today includes more than 275,000 participants and counting, as the Nurses’ Health Study 3 is still recruiting subjects [ 62 ]. The study looks into the risk factors that have been implicated in major chronic diseases among women [ 62 ]. Initially, the study focused on heart disease, cancer, smoking, and contraceptive methods [ 61 ]. As the study evolved, it investigated many other lifestyle factors, characteristics, and diseases [ 61 , 63 ].

The original cohort of the study has been followed up on by mail every two years, with a minimum response rate of 90% [ 61 ]. The second cohort, under NHS 2, was enrolled in 1989 and included 116,430 women. These also were followed up on using mail every two years. A food frequency questionnaire was added in 1991 and was mailed out every four years, with a response rate of 85–90%. Later on blood and urine samples were collected from participants [ 61 ]. The third cohort, under NHS 3, was enrolled in 2010 and is still enrolling, with a goal of diversifying the study population to include other ethnic backgrounds [ 61 ].

3.2.2. Study findings

The study website ( http://www.nurseshealthstudy.org/about-nhs/key-contributions-scientific-knowledge ) covers numerous study findings, such as reporting lower risk of colon cancer and polyps with higher levels of vitamin D [ 64 ]. Also among the findings, Giovannucci et al. reported lower risk rates of colon cancer with longer duration of aspirin usage [ 65 ]. Baer et al. reported on mortality related risk factors among the NHS cohort [ 66 ]. Other findings related to breast cancer, CHD, stroke, colon cancer, hip fracture, cognitive function, and eye disease, in relation to cigarette smoking, oral contraceptives, post-menopausal hormone therapy obesity, alcohol, and diet can be found elsewhere [ 64 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]. More recently Colditz et al. summarized the findings and impact of the three NHS studies in an article published in the American Journal of Public Health [ 80 ].

3.2.3. Strengths and weaknesses

With focus on women, it is considered to be the longest and largest running prospective cohort study that investigates the role of lifestyle on health [ 63 ]. Among the strengths of this study is that it included multiple assessments of the various lifestyle characteristics and exposure factors [ 63 , 80 ], in turn, it also contributed to the methodology of lifestyle assessment in general, which has been used in other studies [ 63 , 80 ]. Additionally, it allowed for the calculation of mortality rates [ 63 ]. As for the weaknesses, white women dominated the original cohort, which reflects on the generalizability of the study results [ 4 , 63 ].

3.3. The Caerphilly Prospective Study (CAPS)

3.3.1. overview.

Also known as the Caerphilly Heart Disease Study, this study was conducted in Caerphilly, South Wales, UK, and focused on ischemic heart disease (IHD) in relation to hormones, hemostatic factors, and lipids [ 81 ]. As the study evolved, other investigations were included which looked into cognitive function, stroke and hearing problems [ 81 ].

The study included four phases. In the first phase, 2512 males, ages 45–59 years, were recruited in 1979. The procedures included blood tests, electrocardiogram (ECG), clinical history, lifestyle and IHD related questionnaires [ 81 ]. The second phase ran from 1984 to 1988 and included 447 males. An audiometry test was added to the list of investigations that were included in the first phase [ 81 ]. Phase 3 took place from 1989 to 1993 and added a cognitive function test and a bleeding time test [ 81 ]. Phase 4 was conducted from 1993 to 1997, which included the audiometry and cognitive function tests originally included in the second and third phases, respectively [ 81 ]. Follow up was conducted at a later stage through mail. The study has accumulated in a total of 150 studies and counting [ 81 ].

3.3.2. Study findings

Among the findings of the Caerphilly Prospective study; Elwood et al. showed that adopting a healthy lifestyle was associated with lower rates of chronic disease, as well as less cognitive impairment and dementia [ 82 ]. In other findings, Mertens et al. reported an inverse association between CVD and adopting a healthy diet [ 83 ], while Bolton et al. reported an inverse association between mid-life lung function and arterial stiffness among men [ 84 ]. Additional findings can be found elsewhere [ 85 , 86 , 87 , 88 , 89 , 90 , 91 ].

3.4. Conclusion

The three sections of this chapter covered the two types of cohort studies. Observational studies in general and cohort studies in specific are a good source of information when an experiment is not feasible. Prospective cohort studies provide valuable information when studying the relationship between exposure and outcome. As with any type of study, prospective cohort studies come with advantages and disadvantages that need to be taken into consideration when interpreting the results of these studies.

Conflict of interest

The author(s) declare no conflict of interest.

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Overview of Analytic Studies

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Epi_Tools.XLSX

All Modules

Prospective and Retrospective Cohort Studies

Cohort studies can be classified as prospective or retrospective based on when outcomes occurred in relation to the enrollment of the cohort.

Prospective Cohort Studies

In a prospective study like the Nurses Health Study baseline information is collected from all subjects in the same way using exactly the same questions and data collection methods for all subjects. The investigators design the questions and data collection procedures carefully in order to obtain accurate information about exposures before disease develops in any of the subjects. After baseline information is collected, subjects in a prospective cohort study are then followed "longitudinally," i.e. over a period of time, usually for years, to determine if and when they become diseased and whether their exposure status changes. In this way, investigators can eventually use the data to answer many questions about the associations between "risk factors" and disease outcomes. For example, one could identify smokers and non-smokers at baseline and compare their subsequent incidence of developing heart disease. Alternatively, one could group subjects based on their body mass index (BMI) and compare their risk of developing heart disease or cancer.

 Examples of Prospective Cohort Studies

• The Framingham Heart Study Home Page
• The Nurses Health Study Home Page

Pitfall: Note that in these prospective cohort studies a comparison of incidence between the groups can only take place after enough time has elapsed so that some subjects developed the outcomes of interest. Since the data analysis occurs after some outcomes have occurred, some students mistakenly would call this a retrospective study, but this is incorrect. The analysis always occurs after a certain number of events have taken place. The characteristic that distinguishes a study as prospective is that the subjects were enrolled, and baseline data was collected before any subjects developed an outcome of interest.

Retrospective Cohort Studies

In contrast, retrospective studies are conceived after some people have already developed the outcomes of interest. The investigators jump back in time to identify a cohort of individuals at a point in time before they have developed the outcomes of interest, and they try to establish their exposure status at that point in time. They then determine whether the subject subsequently developed the outcome of interest.

The video below provides a brief (7:31) explanation of the distinction between retrospective and prospective cohort studies.

Link to a transcript of the video

Intervention Studies (Clinical Trials)

Intervention studies (clinical trials) are experimental research studies that compare the effectiveness of medical treatments, management strategies, prevention strategies, and other medical or public health interventions. Their design is very similar to that of a prospective cohort study. However, in cohort studies exposure status is determined by genetics, self-selection, or life circumstances, and the investigators just observe differences in outcome between those who have a given exposure and those who do not. In clinical trials  exposure status  (the treatment type)  is assigned by the investigators . Ideally, assignment of subjects to one of the comparison groups should be done randomly in order to produce equal distributions of potentially confounding factors. Sometimes a group receiving a new treatment is compared to an untreated group, or a group receiving a placebo or a sham treatment. Sometimes, a new treatment is compared to an untreated group or to a group receiving an established treatment. For more on this topic see the module on Intervention Studies .

In summary, the characteristic that distinguishes a clinical trial from a cohort study is that the investigator assigns the exposure status in a clinical trial, while subjects' genetics, behaviors, and life circumstances determine their exposures in a cohort study.

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Content ©2023. All Rights Reserved. Date last modified: August 15, 2023. Wayne W. LaMorte, MD, PhD, MPH

Study Design 101: Cohort Study

• Case Report
• Case Control Study
• Cohort Study
• Randomized Controlled Trial
• Practice Guideline
• Systematic Review
• Meta-Analysis
• Helpful Formulas
• Finding Specific Study Types

A study design where one or more samples (called cohorts) are followed prospectively and subsequent status evaluations with respect to a disease or outcome are conducted to determine which initial participants exposure characteristics (risk factors) are associated with it. As the study is conducted, outcome from participants in each cohort is measured and relationships with specific characteristics determined

• Subjects in cohorts can be matched, which limits the influence of confounding variables
• Standardization of criteria/outcome is possible
• Easier and cheaper than a randomized controlled trial (RCT)

Disadvantages

• Cohorts can be difficult to identify due to confounding variables
• No randomization, which means that imbalances in patient characteristics could exist
• Blinding/masking is difficult
• Outcome of interest could take time to occur

Design pitfalls to look out for

The cohorts need to be chosen from separate, but similar, populations.

How many differences are there between the control cohort and the experiment cohort? Will those differences cloud the study outcomes?

Fictitious Example

A cohort study was designed to assess the impact of sun exposure on skin damage in beach volleyball players. During a weekend tournament, players from one team wore waterproof, SPF 35 sunscreen, while players from the other team did not wear any sunscreen. At the end of the volleyball tournament players' skin from both teams was analyzed for texture, sun damage, and burns. Comparisons of skin damage were then made based on the use of sunscreen. The analysis showed a significant difference between the cohorts in terms of the skin damage.

Real-life Examples

Hoepner, L., Whyatt, R., Widen, E., Hassoun, A., Oberfield, S., Mueller, N., ... Rundle, A. (2016). Bisphenol A and Adiposity in an Inner-City Birth Cohort. Environmental Health Perspectives, 124 (10), 1644-1650. https://doi.org/10.1289/EHP205

This longitudinal cohort study looked at whether exposure to bisphenol A (BPA) early in life affects obesity levels in children later in life. Positive associations were found between prenatal BPA concentrations in urine and increased fat mass index, percent body fat, and waist circumference at age seven.

Lao, X., Liu, X., Deng, H., Chan, T., Ho, K., Wang, F., ... Yeoh, E. (2018). Sleep Quality, Sleep Duration, and the Risk of Coronary Heart Disease: A Prospective Cohort Study With 60,586 Adults. Journal Of Clinical Sleep Medicine, 14 (1), 109-117. https://doi.org/10.5664/jcsm.6894

This prospective cohort study explored "the joint effects of sleep quality and sleep duration on the development of coronary heart disease." The study included 60,586 participants and an association was shown between increased risk of coronary heart disease and individuals who experienced short sleep duration and poor sleep quality. Long sleep duration did not demonstrate a significant association.

Related Formulas

• Relative Risk

Related Terms

A group that shares the same characteristics among its members (population).

Confounding Variables

Variables that cause/prevent an outcome from occurring outside of or along with the variable being studied. These variables render it difficult or impossible to distinguish the relationship between the variable and outcome being studied).

Population Bias/Volunteer Bias

A sample may be skewed by those who are selected or self-selected into a study. If only certain portions of a population are considered in the selection process, the results of a study may have poor validity.

Prospective Study

A study that moves forward in time, or that the outcomes are being observed as they occur, as opposed to a retrospective study, which looks back on outcomes that have already taken place.

Now test yourself!

1. In a cohort study, an exposure is assessed and then participants are followed prospectively to observe whether they develop the outcome.

a) True b) False

2. Cohort Studies generally look at which of the following?

a) Determining the sensitivity and specificity of diagnostic methods b) Identifying patient characteristics or risk factors associated with a disease or outcome c) Variations among the clinical manifestations of patients with a disease d) The impact of blinding or masking a study population

Evidence Pyramid - Navigation

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• Case Reports
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• Next: Randomized Controlled Trial >>

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Statistical Approaches for Epidemiology pp 57–75 Cite as

Cohort Studies

• Deepa Valvi 2 &
• Steven Browning 3  
• First Online: 13 December 2023

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This chapter provides readers with the critical concepts to consider in designing and analyzing cohort studies. A cohort study is a powerful tool and suitable choice for conducting research for rare exposures in human populations. It can be used to assess associations between multiple exposures and multiple outcomes and identifying the risk factors and causes of diseases. The learning objectives of this chapter are as follows: (1) introduce a cohort study and its uses; (2) describe the design of a cohort study; (3) explain and differentiate the different types of cohort studies including retrospective, prospective, and ambi-directional designs; (4) discuss the selection of study populations, with sources of information on exposure, outcomes, and other key variables; (5) describe approaches to data analysis, including the calculation of person-time, with examples; (6) provide various examples of cohort studies; (7) explain advantages and disadvantages of cohort studies; and (8) discuss potential biases in cohort studies.

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Valvi, D., Browning, S. (2024). Cohort Studies. In: Mitra, A.K. (eds) Statistical Approaches for Epidemiology. Springer, Cham. https://doi.org/10.1007/978-3-031-41784-9_4

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The three-minute appraisal of a prospective cohort study

Ammar bookwala.

McMaster University, Hamilton, Canada and AO-Clinical Investigation, Zurich, Switzerland

Nasir Hussain

Mohit bhandari, i ntroduction.

The prospective cohort study (PCS) is a valuable tool with important applications in epidemiological studies. The study involves the comparison of a cohort of individuals displaying a particular exposure characteristic, with a group of individuals without the exposure characteristic in the format of a longitudinal study. 1 PCSs offer researchers the advantage of measuring outcomes in the real world without the ethical and logistical constraints faced by randomized control trials (RCT). However, PCSs face concerns with internal validity due to the presence of selection bias and confounding variables. The purpose of this paper is to provide clinicians with guidelines for the critical appraisal of a PCS [ Table 1 ].

A three-minute checklist for the critical appraisal of a PCS

K EY C RITERIA FOR C RITICAL A PPRAISAL

Step 1: what are the comparison groups in the study.

Comparison groups in a study may include a control group compared to a treatment group; a treatment group compared to another treatment group; or a control group compared to a variety of treatment groups. The first step when assessing a study design is to determine the comparison made between groups. 2 Well-written studies should have clear guidelines for the selection of all groups. The next step involves assessing the applicability of the study's design to clinical populations. 2 For instance, if you are interested in assessing whether a drug prescribed for hypertension is better than other commercially available drugs, you would want to refer to a study comparing side effects of the drug of interest to other hypertension medication; rather than comparing it to a study examining effects of multiple drug classes. Finally, it is also important to evaluate study design for potential selection biases. 2 In order to minimize the presence of selection bias in studies, baseline characteristics should be relatively consistent between groups. Some sources of selection bias are quite evident for example, sending the treatment group patients to a specialist for surgery and control group or another treatment group's patients to a regular surgeon. Some sources such as channelling bias are subtle and may involve older patients being allocated to a specialist surgeon only, because they constitute a high-risk population. When reading an article, it is important to evaluate both sources and interpret effects on the results of the study

Step 2: How do confounding variables impact the study?

Confounding variables correlate positively or negatively with the independent or dependent variable. Confounding variables are viewed as the principal contributor to a false positive test. PCSs are vulnerable to both known and unknown confounders because patient allocation is not randomized. 3 A literature review of material on the topic can help identify known confounding variables in study designs. When assessing a PCS for confounding variables, it is important to assess the information provided on confounders present in the intervention and comparison groups. 3 Most of the papers provide this information in tabular format. One must also consider methods used to assess for differences in potential confounders between the two groups. Common tests used include the χ 2 test or t -test; however, the significance is sensitive to sample size which can make test results significant but not clinically meaningful. 3 Another strategy is to use standardized differences to examine between group differences. This measure is not as sensitive to sample size as traditional tests, and provides a sense of relative magnitude of difference (differences greater than 0.1 are considered to be meaningful).

Step 3: What analytical strategy was used to assess results?

Finally, one must consider the analytical strategy used to assess results. A common analysis technique used is a regression analysis which looks at the relationship between an independent and dependent variable after adjusting for the effects of other independent variables. 4 Another technique known as stratification involves dividing data into homogenous subgroups, followed by sampling for potential confounding variables among each subgroup. When assessing a study, it is important to look at the analytical strategy used, and which confounders were incorporated in the analytical model. 4 It is also important to check the difference between the adjusted and unadjusted results. A large difference implies significant differences between baseline characteristics of the cohort subgroups, indicating a risk of selection bias. One must also consider is the credibility of the results, which can be assessed using a sensitivity analysis. The sensitivity analysis simulates the size and level of imbalance created by a potential confounder, which allows one to determine the extent to which confounders were incorporated in the creation of the study design. 4 Lastly, one should consider the biological validity of the results. This is a relatively convoluted question, and the answer varies among different studies. However, readers can avoid confusion by comparing results to relatively similar cross-sectional studies.

Practical example

Article: Orosz GM, Magaziner J, Hannan EL, Morrison RS, Koval K, Gilbert M, et al . Association of timing of surgery for hip fractureand patient outcomes. JAMA 2004;291:1738-43. 5

Study objective: To examine the association of timing of surgical repair of hip fracture with function and other outcomes.

Selection of comparison groups

• Comparing function and other outcomes among patients eligible for early (≤24 hours) and late (>24 hours) surgery.
• Incidence of hip fractures relatively high in the field of orthopedic surgery making this clinically relevant.
• To lower the risk of selection bias, the investigators adjusted the analysesfor a range of variables used by clinicians; used propensity scoremethods to match cases of early and late surgery andrepeated the analyses by excluding patients who might not be appropriatecandidates for early surgery.

Impact of confounding variables

• Previous studies yield conflicting results on relationship between early hip fracture surgery and mortality. No information about relationship with functional outcomes.
• Propensity score had aC statistic of 0.68. Indicates no significant difference in baseline characteristics of both cohorts.
• Two types of sensitivity analysis performed; one using propensity scores and one examining what happened if patients who were not candidates for early surgery were excluded.

Analytical strategy used

• Least squares regression (for continuous outcomes),logistic regression (for binary outcomes), and Cox proportional hazards regression for main analyses. Controlled for 18 different variables (age, history of diabetes, hospital site, etc.).
• Difference of 0.04-1.94 between adjusted and unadjusted results indicates consistent baseline characteristics between cohort subgroups.
• Physicians should aim for early operative procedures to reduce patients′ pain and length of hospital stay.

C ONCLUSION

Prospective cohort studies are vulnerable to selection bias and confounding factors, which can affect the validity of the results provided. When evaluating these studies, readers must use an organized approach to critically appraise the design and content of the study, as well as the applicability of the results to clinical populations [ Table 2 ].

Do's and do not's when critically appraising a prospective cohort study

Source of Support: Nil

Conflict of Interest: None.

R EFERENCES

ORIGINAL RESEARCH article

Comparison of the efficacy of spinal cord stimulation and dorsal root ganglion stimulation in the treatment of painful diabetic peripheral neuropathy: a prospective, cohort-controlled study.

• Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China

Objective: The aim of this study was to compare the clinical outcomes of spinal cord stimulation (SCS) and dorsal root ganglion stimulation (DRG-S) in the treatment of painful diabetic peripheral neuropathy (PDPN).

Methods: In this prospective cohort study, 55 patients received dorsal column spinal cord stimulation (SCS group) and 51 patients received dorsal root spinal cord stimulation (DRG-S group). The primary outcome was a Numerical Rating Scale (NRS) remission rate of ≥50%, and secondary outcomes included the effects of SCS and DRG-S on quality of life scores (EQ-5D-3L), nerve conduction velocity, and HbA1c, respectively.

Results: The percentage of NRS remission rate ≥ 50% at 6 months was 80.43 vs. 79.55%, OR (95% CI): 1.06 (0.38–2.97) in the SCS and DRG-S groups, respectively, and the percentage of VAS remission rate ≥ 50% at 12 months was 79.07 vs. 80.95%, OR (95% CI): 0.89 (0.31–2.58). Compared with baseline, there were significant improvements in EQ-5D and EQ-VAS at 6 and 12 months ( p  < 0.05), but there was no difference in improvement between the SCS and DRG-S groups ( p  > 0.05). Nerve conduction velocities of the common peroneal, peroneal, superficial peroneal, and tibial nerves were significantly improved at 6 and 12 months compared with the preoperative period in both the SCS and PND groups ( p < 0.05). However, at 6 and 12 months, there was no difference in HbA1c between the two groups ( p  > 0.05).

Conclusion: Both SCS and DRG-S significantly improved pain, quality of life, and lower extremity nerve conduction velocity in patients with PDPN, and there was no difference between the two treatments at 12 months.

Introduction

As the world’s population ages, the latest data for 2021 estimate that 536.6 million people worldwide will have diabetes (a prevalence of 10.5 percent) ( 1 ). Painful diabetic peripheral neuropathy (PDPN) occurs in 25 percent of people with diabetes and is a progressive neurological disorder with neuropathic pain symptoms; PDPN manifests as pain and other sensory dysfunctions, including numbness, burning, or tingling. It often leads to insomnia, poor quality of life, mood disorders, and even falls, with an increased risk of foot ulcers and lower limb amputation, with far-reaching health-related quality of life implications and potentially life-threatening consequences ( 2 ). International guidelines recommend amitriptyline, duloxetine, pregabalin, or gabapentin as first-line symptomatic analgesics for patients with DPNP. According to a Cochrane review, the best outcome of any single-agent treatment is 50% pain relief in less than half of patients, with medication restrictions and serious adverse effects ( 3 ). This makes the treatment of DPNP difficult.

Over the years, neuromodulation has achieved remarkable results in the treatment of chronic pain. According to the ‘gate control theory’ proposed by Melzack and Wall ( 4 ), which proposed that epidural electrodes placed on the dorsal side of the spinal cord could disrupt the transmission of nociceptive signals by reversing the stimulation of inhibitory interneurons through Aβ fibres in the spinal cord, thereby attenuating the transmission of nociceptive signals from the spinal cord to the brain. Randomised controlled trials have shown that SCS can result in pain relief of more than 50% on the Numeric Rating Scale (NRS) in approximately 70% of patients with PDPN and can significantly improve quality of life (EQOL-5D) ( 5 – 10 ). Dorsal root ganglion stimulation (DRG-S) is a novel neuromodulation therapy that targets the dorsal root ganglion (DRG) before the afferent spinal sensory neurons. This is very different from SCS, which targets dorsal column fibres. DRG-S is therefore also a valuable neuromodulation intervention for chronic neuropathic pain. In a number of mixed aetiology cohorts, DRG-S has been shown to provide relief of neurogenic pain in complex regional pain syndrome (CRPS), complex regional pain syndrome of the lower extremities, and chronic post-operative pain within 12 months ( 11 – 15 ). Recent studies have shown that the mechanism of pain relief by DRG-S is not dependent on GABA release and it is hypothesised that it may be due to the induction of conduction block via the C-type T junction located in the DRG itself, which acts as a low-pass filter for the conduction of action potentials (nociceptive signals) from the periphery to the spinal cord ( 16 , 17 ).

Unfortunately, most previous clinical trials of SCS and DRG-S have focused only on the effect of implanted electrodes on the level of pain relief in patients with PDPN, neglecting improvements in quality of life, lower limb nerve conduction, and HbA1c. Therefore, we prospectively conducted this study of SCS and DRG-S for the treatment of PDPN, and our primary endpoint was to compare the proportion of NRS remission ≥50% at 6 and 12 months postoperatively between the two groups, and the secondary endpoints were to analyse the quality of life scores (EQOL-5D-3L), HbA1c, and nerve conduction velocity in the peripheral nerves of the lower limbs. This clinical trial comparing SCS and DRG-S for the treatment of PDPN provides the latest research on the use of different neuromodulation techniques in the treatment of lower limb pathologic pain and provides clinicians with more clinical decision support when individualising treatment for patients with PDPN.

Materials and methods

Research design.

This is a prospective, cohort-controlled study. The study was conducted from January 2020 to January 2023 at the Neurosurgery Outpatient Clinic of Shengjing Hospital, China Medical University. The study was approved by the medical ethics committee of the hospital (2019PS869J), and the Declaration of Helsinki was adhered to in all procedures. All patients signed an informed consent form before participating in the study. To reduce data bias, two physicians used a blinding method to collect patient information and repeated the measurements multiple times to reduce random errors.

Patients’ pain levels were assessed preoperatively and at 6 and 12 months postoperatively using the Numerical Rating Scale (NRS), the most widely used scale for the assessment of chronic pain. The European 5-Dimensional Quality of Survival Scale (EQ-5D-3L) is the most widely used scale for measuring health-related quality of life and consists of the EQ-5D descriptive system (five dimensions of mobility, self-care, activities of daily living, pain, anxiety, and depression, each with three levels) and a visual scale (EQ-VAS). The EQ-VAS is a visual analogue scoring tool used to assess a patient’s overall subjective perception of their health. 0 represents the worst health and 100 represents the best health. Lower limb nerve conduction velocity is measured using electrodes placed at fixed locations on the patient’s lower limbs to detect bioelectrical signals in the muscles at rest or during contraction. Lower limb nerve conduction velocity is measured in metres per second (m/s), with slower velocities indicating more severe lower limb neuropathy.

Participants

Inclusion criteria were (1) age 18–80 years. (2) Diagnosis of painful diabetic peripheral neuropathy and stable glycaemic control with glycaemic haemoglobin (HbA1c) below 10% in the previous 3 months. (3) Unsatisfactory pain relief with conventional medications with an NRS score ≥ 5 (NRS: 0 means ‘no pain’ and 10 means ‘worst pain imaginable’). (4) Consent to participate in the study and actively participate in the postoperative follow-up. The exclusion criteria were: (1) Diagnosis of other painful peripheral neuropathies based on clinical history and review of medical records. (2) Contraindications to surgery or inability to tolerate surgical treatment, such as cardiopulmonary dysfunction. (3) Pregnancy, lactation and severe systemic disease.

Surgical procedures

A paddle-like SCS surgical lead (SPECIFY 5-6-5, Medtronic, Inc.) was inserted into the epidural space through an intervertebral midline approach while the patient was under local anaesthesia and in the prone position. The Specify 5-6-5 lead was placed over the spinal cord segment receiving the dorsal root fibres (T10-T12) corresponding to the area of pain. The stimulation protocol was individualised for each patient in order to provide maximum relief from neurogenic pain. One week after the stimulation trial, the previous incision was reopened and the implanted leads were connected to the implantable pulse generator (IPG) using a lead connector.

DRG-S group

Under general anaesthesia, two wires (SPECIFY 2 × 8, Medtronic, Inc.) were advanced into the epidural space under fluoroscopic guidance until they entered the intervertebral foramina near the lumbar ganglion at L4-L5 bilaterally. As part of intraoperative device programming, the appropriate location of the lead was determined by overlapping areas of paresthesia and pain. If overlap of pain and paresthesia was not achieved, the lead was repositioned and reprogrammed under fluoroscopy, and the IPG was implanted 1 week after the trial.

Device programming

Pre-programming of the devices was performed by a clinical device technician employed by the device manufacturer according to an established protocol. Individual reprogramming (40–60 Hz, 180–240 μs, 0.5–2.0 V) was performed in both groups of patients using a programming controller (Medtronic, model 97745) under remote control of the technician.

Statistical methods

Based on the results of a previous study in which the proportion of SCS relieving NRS ≥50% was 60% ( 5 – 7 ), with a two-sided test level of α  = 0.05 and a power of 1− β  = 0.8, a minimum of 90 participants would be required to determine the superiority or inferiority of the DRG-S, taking into account a 20% loss to follow-up rate, as calculated by SPSS.

Data were analysed using SPSS 22.0 software and plotted using GraphPad Prism. Continuous variables are expressed as mean (standard deviation). Categorical variables are expressed as percentages. Changes in between- and within-group variables were compared between the SCS and DRG-S groups at pre-treatment, 6 and 12 months post-treatment using paired-samples t -tests. Differences of p  < 0.05 were statistically significant. Differences in primary endpoints were compared between groups using paired t-test analyses based on the minimum clinically important difference (MCID).

Follow-up and baseline characteristics

Of the 191 screened patients, 106 with PDPN who met the inclusion criteria participated in this study: SCS group ( n  = 55), DRG-S group ( n  = 51). After the final stimulation trial, the SCS group ( n  = 51) and the DRG-S group ( n  = 49) underwent permanent implantation of IPGs ( Figure 1 ). The baseline characteristics and PDPN-related medical history of patients in the 6- and 12-month follow-up groups, including age, sex, body mass index (BMI), duration of diabetes, duration of pain symptoms, glycated haemoglobin (HbA1c), type of diabetes, NRS score, and quality of life score (EQOL-5D-3L), were equipotent and comparable at p  > 0.05 ( Table 1 ).

Figure 1 . Flowchart of the study process. * In this study, 46 people in the SCS group completed the 6-month follow-up and 43 people completed the 12-month follow-up; 44 people in the DRG group completed the 6-month follow-up and 42 people completed the 12-month follow-up.

Table 1 . Patient demographics and baseline characteristics.

Numerical rating scale

Regardless of whether the MCID for consideration of the NRS was 2 or the recently recommended 0.9–2.7 ( 18 – 20 ). At 6-month follow-up, there was a reduction of 5.32 (1.74) and 4.93 (1.65) in the SCS group of 2.98 (1.49) and the DRG-S group of 3.16 (1.10) compared with baseline, respectively, p  < 0.001, however there was no difference in the degree of remission between groups, p  = 0.325; at 12-month follow-up, there was a reduction of 5.05 (1.78) and 5.10 (1.48) in the SCS group 3.24 (1.41) and 3.02 (0.92) in the DRG-S group, respectively, p  < 0.001, compared to baseline, and again there was no difference in the degree of remission between groups, p  = 0.890 ( Figure 2 ; Table 2 ).

Figure 2 . Changes in NRS in the SCS and DRG-S groups at baseline, 6 and 12 months. * p  < 0.05; ** p  < 0.01; *** p  < 0.001; NS, p  > 0.05.

Table 2 . Changes in pain and quality of life at different time points in SCS and DRG-S groups.

More importantly, we observed a  ≥ 50% reduction in NRS at 6 months in 37 (80.43%) of SCS group 46 and 35 (79.55%) of DRG-S group 44, OR (95% CI): 1.06 (0.38–2.97) for both groups; and at 12 months in 34 (79. 07%) of SCS group 43 and 34 (80.95%) of DRG-S group 42 ( Table 3 ), the NRS was reduced by ≥50%, OR (95% CI): 0.89 (0.31–2.58), which was not statistically different.

Table 3 . Comparison of primary endpoints in the SCS and DRG-S groups.

Quality of life score (EQOL-5D-3L)

The MCID based on the EQOL-5D was 0.074 ( 21 ), which was a significant improvement ( p  < 0.001) compared to baseline at 6 months in the SCS group 0.58 (0.10) and in the DRG-S group 0.61 (0.10) and at 12 months in the SCS group 0.61 (0.07) and in the DRG-S group 0.59 (0.10). There was no significant difference between the two groups in terms of improvement at 6 and 12 months ( Figure 3A ; Table 2 ). Similarly, EQOL-VAS improved in both groups at 6 and 12 months postoperatively ( Figure 3B ), and there was no difference in improvement between the two groups ( Table 2 ).

Figure 3 . SCS and DRG-S group changes in EQ-5D and EQ-VAS at baseline, 6 and 12 months. * p  < 0.05; ** p  < 0.01; *** p  < 0.001; NS, p  > 0.05. (A) Changes in EQ-5D at baseline, 6 and 12 months in the SCS and DRG-S groups; (B) Changes in EQ-VAS at baseline, 6 and 12 months in the SCS and DRG-S groups.

Nerve conduction velocity

Compared to baseline, nerve conduction velocities of the common peroneal, peroneal, superficial peroneal and tibial nerves in the lower limbs of patients in the SCS and DRG-S groups were significantly improved at 6 and 12 months postoperatively, respectively, p  < 0.05. However, there was no significant difference in the changes between 6 and 12 months ( Figures 4A , B ).

Figure 4 . Changes in lower limb nerve conduction velocities at baseline, 6 and 12 months in the SCS and DRG-S groups. * p  < 0.05; ** p  < 0.01; *** p  < 0.001; NS, p  > 0.05. (A) Lower limb nerve conduction velocity changes in SCS group at baseline, 6 and 12 months; (B) Lower limb nerve conduction velocity changes in DRG-S group at baseline, 6 and 12 months.

Glycaemic haemoglobin

At 6 months, HbA1c was 7.86 (0.75) in the SCS group and 7.92 (0.66) in the DRG-S group. At 12 months, HbA1c was 7.90 (0.71) in the SCS group and 7.95 (0.77) in the DRG-S group. There was no significant difference in HbA1c between the two groups at either 6 or 12 months ( Table 4 ).

Table 4 . Comparison of HbA1c (%) in the SCS and DRG-S groups.

This prospective study showed that patients in both the SCS and DRG-S groups had significantly lower NRS scores at both the 6- and 12-month follow-up compared to baseline. There was no significant difference between the SCS and DRG-S groups at 12 months compared to 6 months. Importantly, the proportion of patients with NRS remission ≥50 in the SCS group was 80.43 and 79.07% at 6 and 12 months, respectively, whereas the proportion of patients with NRS remission ≥50 in the DRG-S group was 79.55 and 80.95%, respectively, and there was no difference between the two groups. In addition, EQ-5D, EQ-VAS and lower limb nerve conduction velocities were significantly improved in all patients who attended follow-up. There was no significant difference in HbA1c between the two groups Thus, we compared that both SCS and DRG-S could improve lower limb nerve conduction function, reduce pain levels and improve quality of life in patients with PDPN.

In a prospective RCT long-term study of SCS, 13 of 22 patients (59%) had ≥50% NRS remission at 6 months; 11 of 17 (65%) had ≥50% NRS remission at 24 months, with 9 (53%) reporting a significant improvement in quality of life; and 7 of 22 (32%) had ≥50% NRS remission at 5 years. 80% of patients with permanent implants were still using their SCS device at 5 years ( 5 – 7 ). A recent RCT showed that when 10 kHz SCS was combined with conventional medical management (CMM), 75 of 95 patients (79%) had >50% pain relief on the Visual Analogue Score (VAS) at 6 months and 121 of 142 patients (85%) had >50% pain relief at 12 months ( 8 – 10 ). In another prospective multicentre RCT comparing DRG-S and SCS in the treatment of complex regional pain syndrome (CRPS), the proportion of DRG-S and SCS groups achieving treatment success (>50% pain relief) at 3 months was 81.2% (56/69) vs. 55.7% (39/70). Percentage of success (74.2%; 49/66 vs. 53.0%; 35/66) ( 15 ). These results are consistent with our findings that both SCS and DRG-S are important tools in the treatment of PDPN in terms of neuromodulation and can achieve significant therapeutic results. However, in contrast to other studies, our study did not only compare the improvement in NRS between the two groups as a whole, but also individualised the improvement in each patient ( Figure 2 ). In terms of quality of life, we used the internationally recognised EQ-5D-3L scoring system in our study design. These are the strengths of this study over the existing literature. In addition, our study also showed that the SCS and DPG-S techniques have an effect on HbA1c in diabetic patients.

From an analgesic mechanism perspective, SCS consists of epidural electrodes in the dorsal columns of the spinal cord to excite inhibitory interneurons to release γ-aminobutyric acid (GABA) by retrogradely stimulating Aβ fibres in the spinal cord, which interrupts the transmission of nociceptive signals from the spinal cord to the brain, thereby attenuating the nociceptive signals; dorsal columns of the Aβ fibres can also be positively stimulated to produce paresthesia in the areas innervated by the fibres, masking the painful sensation and thus achieving pain relief ( 16 ). With regard to the DRG-S, one study found that there is an extensive GABAergic network between the cell bodies of DRG neurons, and that sensory neurons in the DRG have the ability to express the key proteins required for GABA synthesis and release, and can release GABA when DRG neurons receive a stimulus ( 22 ). It has been suggested that DRG-S, like SCS, relies on stimulation of Aβ fibres in the dorsal horn of the spinal cord and release of GABA to activate the pain gating mechanism and inhibit nociception ( 23 , 24 ). Interestingly, however, a recent study found that the analgesic effect of DRG-S does not depend on the release of GABA in the dorsal horn of the spinal cord ( 25 ). Although the analgesic mechanism of DRG-S is highly controversial, it is clear that DRG-S inhibits the excitability of slow pain fibres (C-fibres) ( 26 ).

This study has some limitations. First, the process of grouping patients was not randomised, and therefore there is a possibility of selection bias. Fortunately, a comparison of the baseline values of patients in the SCS and DRG-S groups who attended our follow-up showed that the groups were equivalent at baseline and therefore comparable. Second, the follow-up period was only 12 months, the number of cases was small, and only the traditional tonic stimulation mode (voltage, 0.5 V; pulse width, 180–240 μs; frequency, 40 Hz) was used; other stimulation modes or waveforms, such as burst mode and high-frequency stimulation, were not evaluated. Nevertheless, the current results suggest that both SCS and DRG-S are potentially effective treatments for PDPN. In this trial design, we used a single-blind approach to avoid biassing the results, but there are still some potential effects, including rater bias, treatment effect expectation and data collection bias. Although single blinding is not as advantageous as double or triple blinding designs to completely eliminate these potential effects, it is still a commonly used form of blinding that can reduce subjective bias to some extent.

To our knowledge, this is the first cohort study comparing SCS and DRG-S in the treatment of PDPN in clinical research. Most previous SCS studies have focused on the clinical efficacy of lower extremity pain and quality of life in patients with PDPN, and most DRG-S studies have focused on the treatment efficacy of complex regional pain syndrome (CRPS). The design of this study included both SCS and DRG-S groups and assessed the percentage of patients in both cohorts with Numeric Rating Scale (NRS) relief ≥50% at 6 and 12 months, as well as postoperative quality of life scores (EQOL-5D) and effects on HbA1c. These results provide clinicians with higher quality individualised protocols for the treatment of PDPN.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by the Medical Ethics Committee of Shengjing Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

Y-FH: Conceptualization, Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing. XC: Data curation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

The authors especially thank Min Bao, Doctor of Medicine, Shengjing Hospital of China Medical University, for technical assistance. The authors thank the patients who participated in the study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: dorsal root ganglion stimulation, neuropathic pain, diabetic peripheral neuropathy, spinal cord stimulation, PDPN

Citation: Han Y-F and Cong X (2024) Comparison of the efficacy of spinal cord stimulation and dorsal root ganglion stimulation in the treatment of painful diabetic peripheral neuropathy: a prospective, cohort-controlled study. Front. Neurol . 15:1366796. doi: 10.3389/fneur.2024.1366796

Received: 07 January 2024; Accepted: 02 April 2024; Published: 10 April 2024.

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Copyright © 2024 Han and Cong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xi Cong, [email protected]

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