Cohort Study Design

In cohort studies, you directly measure incidence (probability of outcome over time), so Relative Risk (RR) is the natural estimate: RR = Incidence_exposed / Incidence_unexposed. Odds Ratio (OR) is the ratio of odds of outcome; it arises in case-control studies where you cannot measure incidence. In cohorts with rare outcomes (incidence <10%), RR and OR approximate each other. When outcomes are common, they diverge: OR overstates the effect compared to RR. Always report RR in cohort studies; reserve OR for case-control designs.

Person-time (or follow-up time) is the sum of individual follow-up durations. If you follow 100 people for 10 years each, that is 1000 person-years. If 50 people are followed 5 years and 50 people 10 years, that is also 750 person-years. Incidence rate is calculated per person-time unit (e.g., per 1000 person-years). Person-time accounts for the fact that some participants drop out, die, or are censored early. Using person-time as the denominator (rather than just the number of people) gives unbiased estimates when follow-up is variable.

Loss to follow-up is a major source of bias. Aim for >80% follow-up. In analysis, do not simply exclude those lost; this introduces selection bias. Instead, use intention-to-treat logic: classify lost participants by their last known status, and perform sensitivity analyses assuming different outcome scenarios (e.g., all lost are outcome-free, all lost developed outcome, or missing at random). If loss-to-follow-up differs markedly between exposed and unexposed groups, interpret results with caution. Inverse probability weighting (IPW) or multiple imputation can adjust for systematic missingness under the 'missing at random' assumption.

A cohort study enrolls disease-free (or outcome-free) participants, measures exposure at baseline, and follows them forward until outcome occurs. A cross-sectional study measures exposure and outcome simultaneously at a single point in time, capturing prevalence. Cohorts have clear temporal sequence (exposure → outcome), enabling causal inference. Cross-sectional studies cannot establish directionality: did exposure cause outcome, or did outcome influence exposure perception? Cohorts are more costly and time-consuming but provide stronger evidence for causality.