Instructor Resource Chapter 13 Copyright © Scott B. Patten, Permission granted for classroom use with Epidemiology for Canadian Students: Principles, Methods & Critical Appraisal (Edmonton: Brush Education Inc.

Chapter 13. Prospective cohort studies

Objectives Define prospective cohort studies. Define measures of association in prospective cohort studies: risk ratios, incident rate ratios, and hazard ratios. Explain how to interpret measures of association calculated from prospective cohort studies. List strengths and weaknesses of prospective cohort studies.

What is a cohort study? A cohort is a group with a shared characteristic: Special purpose cohort studies assemble cohorts based on exposure and then compare outcomes over time. General purpose cohort studies assemble groups of subjects and then compare outcomes in relation to multiple exposures. Cohort studies can also be divided according to their temporal direction: prospective retrospective Usually, cohort studies are analytical in nature.

Study design classification: prospective cohort studies A study’s logical direction of inquiry involves how it explores cause and effect. The logical direction in case-control studies is backward: these studies start with an effect (cases have the disease) and then look for a cause (exposures). By contrast, cohort studies have a forward logical direction: they start with a possible cause (exposure) and then look for effects (disease).

Study-design classification Logical perspective: cause to effect (forward) Logical perspective: effect to cause (backward) Temporal perspective: retrospective Retrospective cohort study Case-control study Temporal perspective: prospective Prospective cohort study--

Schematic for a general purpose cohort study Cohort Exclude prevalent cases Eligible cohort Exposed Nonexposed Disease outcome (present)(future)

Schematic for a special purpose cohort study Exposed members of source population Exclude prevalent cases Eligible cohort (exposed) Disease outcome Non-exposed members of source population Eligible cohort (nonexposed) Disease outcome Selection (present) (future)

Examples of Canadian multipurpose cohort studies National Population Health Survey (NPHS) National Longitudinal Study on Children and Youth (NLSCY) Canadian Study of Aging Partnership for Tomorrow Project

Assessing associations in prospective cohort studies Ratio-based measures may be: risk ratios incidence rate ratios hazard ratios

2x2 Table for a cohort study Incident disease No incidence disease Total Exposed abn exposed Nonexposed cdn nonexposed

A cautionary note about 2 x 2 tables These tables may look exactly the same as a 2 x 2 table calculated for a cross-sectional or case- control study, but the meaning of the numbers is different. This is also true for formulas that arise from the 2 x 2 tables—e.g. the formula for a risk ratio may look the same as that for a prevalance ratio, but the meaning is different.

The risk ratio

Incidence rate ratio (IRR) This is a ratio of rates rather than risks. It is useful if many members of the cohort do not remain at risk for the whole study interval—e.g. because they develop the disease, die, or drop out of the study.

Incidence rate ratios (IRR)

Hazard ratios Hazard ratios are associated with survival analysis, a technique that is commonly used in the analysis of data from prospective cohort studies, even when mortality is not the outcome of interest. A hazard is the rate of an outcome at a point in time conditional on the outcome not having occurred to that point in time. A hazard ratio is the ratio of 2 hazard rates – that for the exposed divided by that for the nonexposed.

Survival analysis Survival analysis is a sophisticated way of addressing the issues that arise when at-risk subjects leave the at- risk cohort, either because they develop the outcome or for some other reason. Survival analysis addresses this issue by censoring (removing participants from the at-risk cohort) in the calculation of hazard ratios. This approach can be extended to more elaborate models, such as proportional hazard models capable of addressing complex methodological problems

Difference-based measures When calculated from a cohort study, these may be: risk differences incidence rate differences

Selection bias in prospective cohort studies Selection bias comes from study-design defects that affect who does or does not participate in a study. Factors that affect participation include: defects in sampling procedures for obtaining consent problems retaining subjects during follow-up collecting information from subjects* Errors in measurement cause measurement bias, not selection bias, but if failure to measure a variable (missing data) leads to exclusion of data from calculation of a parameter, selection bias may be introduced since those subjects no longer participate in calculation of the parameter.

Selection bias in prospective cohort studies (continued) In prospective cohort studies, subjects are selected (or included) in ways that ensure that none of them has the disease at baseline. Since the disease is not present at the time of selection, it is not possible for disease status to affect selection (if disease occurs at all, it will occur in the future)—a different situation than in case-control studies. This provides prospective cohort studies with a degree of protection against forms of bias that could occur in case-control studies. However, attrition from the study affects participation and can cause selection bias.

Attrition Attrition happens when participants leave a study in ways not planned by the investigators. People can “drop out” from the sample for many reasons: moving away becoming lost to follow-up withdrawing consent other reasons

Attrition (continued) Attrition is almost inevitable in real-world research, and may be influenced by disease, or by precursors of disease, or by exposures that affect behaviour. Of particular concern: attrition can depend on factors related to disease in a way that differs depending on exposure. This is the scenario in which selection bias may occur.

Missing data If there are missing data about the disease outcome, even among respondents who did not withdraw from a study, those respondents are not usually included in the calculation of a study’s estimates. This type of problem can be regarded as a defect of participation, because those who do not provide the necessary data cannot be included in the estimate of association and are essentially not participating in its calculation.

Missing data (continued) Missing data may lead to systematically over- or underestimating the target parameter. If the missing data are unrelated to exposure or disease, and are missing completely at random, bias will not occur. However, if the missing data are related to the disease outcome in a way that differs depending on exposure, selection bias will occur, according to familiar principles.

Misclassification bias in prospective cohort studies Misclassification bias follows similar patterns in prospective cohort studies as in case-control studies. A difference, however, is that it tends to arise from misclassification of disease status rather than exposure status. The mechanism of bias can be differential or nondifferential.

Nondifferential misclassification bias The behaviour of nondifferential misclassification is predictable. The direction of the bias is toward the null value. The null value for a risk ratio, incidence rate ratio, or hazard ratio is 1. The null value for a risk difference is 0.

Differential misclassification bias The direction of differential misclassification bias depends on the nature of misclassification in a particular study.

Diagnostic suspicion bias This is a type of differential misclassification bias. In a cohort study, the exposure status of participants can be accurately measured at the time of selection. However, there may be concern about the subsequent classification of disease status. This is especially true in situations where knowledge of the exposure might affect the accuracy of outcome classification.

Diagnostic suspicion bias (continued) Suspicion that the exposure is a risk factor could cause greater attention to detection of the disease in exposed subjects. This could occur by increased sensitivity or decreased specificity in the exposed respondents. Each possibility would result in differential misclassification, since the accuracy of classification of disease depends on exposure. The direction of bias in each case would be toward a higher estimated effect.

Strengths of prospective cohort studies Temporal clarity: Exposure is confirmed to precede disease. Risk measurement: These studies can measure incidence. Some resilience to selection bias: Prospective cohort studies are often considered less vulnerable to selection bias than case-control studies. However, they are vulnerable to selection bias arising from attrition. Fit with large numbers of outcomes from single exposures: Prospective cohort studies can be very efficient for studying a large number of outcomes from a single exposure.

Weaknesses of prospective cohort studies Expense: They are generally expensive. Time: They are time-consuming, especially in situations where the induction and/or latency periods are long. Rare outcomes: Prospective cohort studies are not very efficient for studying rare outcomes, since only a small number of outcome events may occur. They are good for rare exposures. Attrition: Prospective cohort studies are vulnerable to attrition, which can result in selection bias if the attrition depends on exposure and disease.

End