Cohort Studies

Because of their well-documented advantages, randomized controlled trials (RCTs) represent the gold standard for testing hypotheses in medical research. However, RCTs are not ideal for addressing certain research questions (e.g., the risk for renal insufficiency associated with alcohol intake). In addition, conducting a properly designed RCT often requires substantial time and resources. For these reasons, including barriers of cost and need for rapid answers, the majority of clinical research studies in the renal literature use an observational design. Although there clearly is a great need for more RCTs that are conducted in populations with kidney disease, rigorous observational studies are extremely valuable and in many circumstances yield similar results as rigorous RCTs (1). Furthermore, although RCTs are considered the gold standard for examining the efficacy of a therapy, studies of prognosis often are addressed best by cohort studies. In this first of a multipart series dedicated to reviewing clinical research methods in nephrology, after providing a brief overview of observational studies, we focus on one of the major subtypes of observational designs: The cohort study. A glossary of common terms that are used in this and subsequent sections of this series is included in Table 1. Details on other methods (e.g., case-control studies, RCTs) will be the subject of future reviews. Brief Overview of Observational Studies The simplest form of observational study is the case report or case series, which describes the clinical course of individuals with a particular condition or diagnosis. Such studies often highlight a single clinical condition and at times even suggest a potential biologic mechanism. In a case series, the clinician gains an appreciation of the breadth of abnormalities (e.g., range of proteinuria in patients with HIV-associated nephropathy) that may characterize a single disease process. Although these studies may attempt to identify factors or treatments that may have influenced the outcome, by definition they do not include a control or comparison group without the exposure or outcome and hence cannot support strong associations between the two. In rare circumstances, the data from a case series can be sufficiently compelling to change clinical or regulatory decisions (e.g., the relation between phocomelia and thalidomide), but usually conclusions from these studies must be viewed with extreme caution. In cross-sectional studies such as surveys or chart reviews, exposures and outcomes are ascertained at the same time. Cross-sectional studies have several potential advantages, including low cost, simplicity, and reduced risk for certain types of bias, such what occurs with loss to follow-up (see below). However, systematic differences between those who agree and do not agree to participate (responder bias) can be problematic in cross-sectional studies. In addition, because exposure and outcome are assessed simultaneously, causality cannot be determined conclusively. For example, although elevated serum levels of C-reactive protein (a putative “exposure”) are seen in patients with chronic kidney disease (CKD; the putative “outcome”), it would be misleading to conclude that elevated levels of C-reactive protein predispose patients to CKD when both were ascertained at the same time. Finally, because cross-sectional studies include prevalent rather than incident cases, they may be prone to incidence-prevalence bias. This form of bias occurs because groups of prevalent individuals may be systematically different from incident individuals with the same condition. For example, a high proportion of patients die within 90 d of initiating dialysis, often as a result of serious underlying illness or multiple comorbidities. Because of their shorter survival, there will be less opportunity to enroll these individuals in studies of prevalent patients, although they would be included in a study of incident patients. Case-control studies begin by identifying participants with and without the condition of interest (“cases” and “controls,” respectively). Exposures then are determined retrospectively, and their frequency is compared between cases and controls. Exposures that are more common in cases may give clues to causal relationships, provided that cases and controls are similar in all respects except for the condition of interest. Indeed, selection of appropriate controls is a major challenge in all case-control studies and is a major determinant of whether the conclusion of the study is valid. Case-control studies also require careful consideration of bias relating to differential recall of exposures. Although these requirements can be difficult to meet, the case-control design is inexpensive and efficient, especially when the outcome of interest is rare. Case-control studies will be covered in subsequent sections of this continuing series. Cohort Studies The term cohort refers to a group of individuals who have a common feature when they are assembled and who are followed in time. Therefore, cohort studies begin by ascertaining exposure among a group of individuals who are free of the outcome of interest and evaluate participants for incident events that occur with time (Figure 1). Over time, outcomes may occur in both groups, and the analysis therefore examines the frequency of the outcome in the exposed versus the unexposed groups. The features of cohort studies allow investigators to define the temporal sequence between exposure and outcome and avoid the risk of recall bias (i.e., asking those with or without the outcome of interest to recall a past exposure). Follow-up time should be sufficient for outcomes to occur. For example, a cohort of incident dialysis patients who are followed for 1 yr would allow analyses that are targeted at determining whether a baseline exposure was associated with mortality, because at the end of 1 yr, mortality is expected in >20% of the cohort (2). Participants in prospective cohort studies are identified, classified with respect to exposure status at baseline, and then are followed over time to ascertain outcomes (Figure 1). In historical/retrospective cohort studies (also known as nonconcurrent cohort studies), a group of individuals (the cohort) is identified on the basis of a common feature or features that were determined in the past (e.g., starting dialysis during a particular period; see references [3,4] for examples). Historical cohort studies represent studies in which exposures and outcomes were collected sometime in the past, but the ascertainment of exposures antedated the development of the outcomes, and hence the temporal sequence of events (provided that no subjects have the outcome at the time exposures are measured) is preserved. The concern about historical cohort studies is that exposure data usually are not collected specifically for the study, and, hence, the possibility of missing information or unmeasured confounders is high. The availability of electronic medical records has greatly facilitated the conduct of historical cohort studies, which are efficient, inexpensive, and ideal for less common diseases, especially for those with long latency periods. Both historical and prospective types of cohort studies are well suited to study rare exposures and examining multiple potential effects of a single exposure. Certainly, cohort studies allow testing of multiple hypotheses. However, the possibility of bias relating to multiple comparisons means that analyses and results should be hypothesis driven and biologically plausible (5). Finally, cohort studies, in particular prospective cohort studies in which samples are stored when patients enroll, allow the investigator to go back later and test new hypotheses as they arise. Although recently critical discrepancies between results from observational studies and randomized trials have been highlighted (6), in general, the results from well-conducted cohort studies often are similar to those from randomized trials (1). In fact, in a systematic review of the subject, “well-designed observational studies did not systematically overestimate the magnitude of the associations between exposure and outcome as compared with the results of randomized, controlled trials of the same topic” (1). In examining outcomes among women who received hormone replacement therapy (HRT), although observational studies did not yield similar results in the area of coronary heart disease, the two types of studies yielded almost identical point estimates in the areas of risk for breast and colorectal cancer, hip fracture, stroke, and pulmonary embolism (6). Grodstein et al. (6) systematically reviewed these studies and suggested that methodologic differences may have explained why discrepancies were noted for certain outcomes and not for others. For example, observational data supporting the use of HRT primarily examined women who initiated therapy at the time of menopause, whereas approximately 70% of women in the Women’s Health Initiative (RCT examining HRT and outcomes) were enrolled at the 60 yr or older. Re-analysis of each study, stratified by time of initiation of HRT, are more consistent: A trend toward a benefit with HRT in both types of studies in younger women closer to menopause and a trend toward no benefit and potential harm among older women who initiate HRT several years after onset of menopause (7,8). Therefore, the two types of studies yielded similar results when stratified by timing of HRT initiation. Nevertheless, it is important to remember that in observational studies, exposures among the cohort members are not randomly assigned; therefore, the possibility that “other” differences (e.g., residual confounders) explain differences in exposures is high. Analysis of Cohort Studies The major objective in cohort studies is to compare the risk for an outcome or outcomes in groups that are defined by exposure status. Because participants are free of the outcome at baseline, investigators usually are interested in incident (rather than prevalent) cases. For the purposes of health studies, survival time usually is the metric of index. Therefore, incidence rate (the number of cases per unit of time) generally is of greater interest than the crude incidence (the number of cases). For example, Incidence of ESRD in two general groups, A (n = 10,000) and B (n = 1000) Incidence of ESRD among group A = 1500 new cases between 2001 and 2003 Incidence rate = 1500/3 yr, or 500 cases per year Incidence of ESRD among group B = 300 new cases between 2001 and 2003 Incidence rate = 300/3 yr = 100 cases per year Because age and gender are such fundamental properties of populations (and often are associated with outcomes), incidence rates often are age and gender adjusted. However, for purposes of this example, we assume that age and gender are distributed evenly between the two groups and, thus, that the incidence rates for the two groups are similar with and without adjustment. In this example, the incidence and incidence rate of ESRD both are higher for group A than group B. Of greater epidemiologic interest is the relative risk or risk ratio (RR; Table 2): In this case, the higher incidence rate of ESRD in group A is driven by the larger prevalence of this group in the total population (10,000 in A/11,000 in A + B), because group A actually is at lower risk for ESRD (reflected by the RR of <1). In this case, the period of observation (2001 to 2003) was the same for both groups. However, because this is not always the case, the denominator for the RR can be a measure of person-time (i.e., person-years at risk) rather than the number of people at risk. This latter point is especially important because each subject may have different follow-up times, each contributing a different number of person-years to the denominator. Person-years of follow-up then can be normalized for each group (e.g., number of cases per 1000 person-years) for interpretation and comparison purposes. For certain outcomes (e.g., mortality, renal allograft failure), it may be particularly relevant to consider the time until the event occurs, rather than the incidence of the event. To give an absurd example, the incidence of death would be equal in all subgroups of a cohort study after two centuries had elapsed, regardless of any true association between exposure and risk. However, even when the outcome is not inevitable, refining estimates of risk by considering time to event usually results in increased statistical power compared with analyses that simply evaluate whether the event occurred. Two commonly used approaches for analyzing time-to-event data include Kaplan-Meier analysis (which allows univariate comparison of survival times between groups) and Cox proportional hazards analysis (which allows both univariate and multivariate comparisons). Kaplan-Meier plots are used to display graphically time to event, often comparing survival of groups who have different baseline exposures with frequency of the event on the y axis and time on the x axis (Figure 2). The statistical test to compare one curve with another is the log-rank test, which is a simple modification of the χ2 test. Although not all individuals who enter the study will reach the end point of interest (not all subjects die) during the study period, it also is important to remember that subjects may leave the study for reasons other than the primary outcome before the end of the follow-up period (e.g., leaving the cohort because of recovery, moving to a different state); therefore, the exact survival time or time to the event will be unknown. Nevertheless, these subjects contribute person-time information until they are censored. Unfortunately, if subjects leave the study for reasons related to their exposure or outcome (informative censoring), then the observations may become distorted (e.g., sicker patients leave the study before they die), and the results then may be erroneous. Sources of Error in Cohort Studies: Generalizability, Bias, Confounding, and Chance Compared with randomized trials (which often study a select group of patients on the basis of inclusion and exclusion criteria), observational studies often include participants with a wider spectrum of disease severity and comorbidity (1). Therefore, results from cohort studies that highlight the effects of treatment may have better external validity (generalizability to the affected population) than those from randomized trials. However, there is a higher risk for drawing incorrect inferences about treatment effects from cohort studies because of the increased likelihood of bias (because treatments are not randomly assigned); therefore, results from observational studies should be confirmed by randomized trials whenever possible. Even in cases in which a randomized trial is not feasible, the possibility of biased results from a cohort study remains. Bias occurs when results of a study systematically deviate from the truth because of nonrandom factors. Although many specific types of bias have been described, there are three broad categories: Selection bias, information bias, and Selection bias occurs when study participants are not of the population at risk for the This is particularly relevant for case-control studies, in which the for cases and controls must be as similar as possible. However, selection bias also can occur in cohort studies. of selection bias that can occur in cohort studies is bias, which occurs when participants with disease are enrolled because their more course allows a period for (and a higher likelihood of This is relevant because and expected to treatment all may in those with versus of types of selection bias can occur when loss to follow-up is high because the likelihood of follow-up may be related to the exposure and outcome study. bias occurs when data on exposure or outcome are systematically when the exposure is in people with the outcome or when the likelihood of the outcome between exposed and unexposed For example, the risk for associated with be in a cohort study that used data from clinical which were on as of to define the The risk to be greater if were more to for in people with cases that be in those without or if the of for the likelihood that the exposure would be of exposure or outcome that occurs at will to bias toward the (the that exposure and outcome are not Although statistical can be used to types of selection and exposure bias, these are best by careful study design. the study design cannot be analyses (e.g., that all participants who were to follow-up actually the see for can about the potential of these occurs when a is associated with both exposure and A of is the observation that are at higher risk for In this example, is a because it is associated with both the exposure are more to and the outcome in would be a study of and risk for kidney in which an exposure (e.g., is to be associated with an outcome (e.g., kidney failure), the exposure may be the result of a (e.g., illness that is with kidney that is with the outcome and, thus, the (e.g., illness related to kidney the between the exposure of interest and should not be in the causal between exposure and For example, in studies of the relation between and reduced be associated with both the exposure and the outcome but may not the association between the two because reduced be the underlying for increased Although and statistical can for it is important to that these for potential confounders that have been Because such factors may be or potential for residual (i.e., that to for potential confounders) even in the conducted cohort studies. associations with or in risk) were to be at low risk for residual The frequency of cohort studies with means that associations may be a in relative risk. In to their lower clinical such associations also are more to be as a result of residual Although cohort studies important for hypotheses about therapy, they cannot randomized trials. This is particularly relevant to the several in which observational data had a on clinical before well-conducted randomized trials their A by which cohort studies may incorrect conclusions is by statistical is at the an exposure will be to be associated with the outcome in Because cohort studies may data on of potential there can be substantial potential for results because of multiple to the use of analysis that an exposure is associated with the outcome in participants with certain potential can be by the of a statistical for multiple and for Although associations that to be biologically plausible be more to be potential often are to even when no relation between exposure and outcome on biologic may to of a association with supporting and and a cohort study can be time and the specific group of individuals (e.g., incident versus prevalent dialysis to be followed and and the of the exposures to ascertain at baseline and follow-up represent of the to exposures is to randomized trials follow-up cohort studies, of the of data that were collected in the past is the case with all cohort studies, supporting the of the all relevant outcomes, and loss to follow-up represent of the known prospective cohort studies that were primarily to study outcomes but examined renal end include the and the Health The assembled individuals who were free of heart disease in examined each for of coronary heart disease, and strong associations between specific baseline exposures (e.g., and coronary heart disease over yr of an of exposure including renal and the investigators have examined renal outcomes in both a cross-sectional and a prospective the of cohort studies, specific cohort studies also have in the past (e.g., for in for ESRD and of and for Studies of In the of specific rather than we highlight two prospective cohort studies Cohort In to the of CKD and associated disease and to the of and and the Cohort in 2001 for this prospective cohort study on identifying risk factors for and mortality and risk factors for toward for has been with the of to reach and participants who are to and have a of chronic renal targeted individuals with specific from to or per on and the follow-up time is included patients with kidney disease, those who are on for and those who have or will be collected study as well as include of renal and and of of will focus on of renal and for cohort and of of are Therefore, for individuals with a prospective cohort study with and of this important cohort study will require a of resources. in In et al. initiated a prospective cohort study of incident patients who were chronic in one of dialysis the that are by The primary of the study by and then by the of and and is to identify risk factors and potential that are in the in baseline d of initiating chronic and 90 d and standard and serum and samples are samples are to in the for that are and by and are stored for use in future incident patients, regardless of or of were and participants with baseline samples have enrolled as of will be followed for 1 yr from the initiation of The of this prospective cohort study is the to in that may outcomes such as exposures and primary outcomes (e.g., are collected and of stored will bias because all samples are collected in a similar without of exposure or outcome status. Because all patients who in the have their data and collected on a basis and information on patients who leave the (e.g., is information is expected to be all cohort studies, this study not information on all potential is subject to by because treatments are not and is subject to other including loss to Cohort studies, in particular prospective cohort studies, several important over other of observational studies. Cohort studies at the of the of observational studies exposure to Indeed, cohort studies often yield results that those from Furthermore, for certain RCT may not be for or Nevertheless, cohort studies to bias and although these can be with study design. of the medical literature should be with cohort studies because such studies are In should their and and when cohort studies in general or a specific cohort in particular (because not all are and in a similar can or cannot test the hypothesis in Certainly, the support for any between exposure and outcome on a single study, regardless of study and requires support from a of and clinical design of prospective cohort study in which a population at risk is identified, exposures are defined at baseline, and the cohort is followed in time. occur in both groups, and the incidence of events among the exposed is compared with the incidence among the unexposed Kaplan-Meier used to events (e.g., time to of three groups and on each curve represent data (e.g., leaving the study for reasons to the an event (e.g., a is or the population is therefore, a death after that point a higher proportion of the as a result of a single event larger as the curve to the the in each group that at each time point is the and the often is by a statistical test (e.g., to differences between risk of ESRD in two groups of study was by and and for and for Health

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