Holtfreter, Birte

ABSTRACT Cohort studies are vulnerable to confounding and time‐related biases. Recent advances emphasize the importance of emulating a hypothetical target trial to minimize several of these biases. The target trial framework is a critical tool for designing cohort studies. It requires explicit specification of protocol components: eligibility criteria, treatment strategies, outcomes, timing of follow‐up (time zero), and the causal contrast to be estimated. Critical to this framework is defining “time zero” when treatment assignment takes place and follow‐up begins, which prevents prevalent user and immortal time bias and ensures proper temporal sequencing of confounders, exposure, and outcome. By guiding design, data collection, and analysis, the target trial framework helps align observational studies with the principles of randomized trials, improving the validity and interpretability of their findings. While it cannot eliminate unmeasured confounding, this approach promotes transparent, bias‐conscious cohort design, minimizes time‐related bias, and supports more robust causal inference with observational data.

ABSTRACT Cross‐sectional studies capture health states, exposures, and risk factors at a single time point, providing essential data for estimating disease prevalence and informing public health planning. These studies serve multiple epidemiological purposes: characterizing population health, monitoring temporal trends through repeated surveys, and evaluating interventions via interrupted time series designs. They also offer practical advantages for validating self‐reported measures and creating diagnostic models. Cross‐sectional designs are efficient and well‐suited to descriptive epidemiology, but they have limited utility for causal inference. The simultaneous measurement of exposures and outcomes creates temporal ambiguity that fundamentally constrains etiologic interpretation. However, causal inferences can be strengthened under specific conditions—when temporal sequence is unambiguous (e.g., genetic variants preceding outcomes) or when valid instrumental variables are available. This methodological tutorial equips readers with concepts and tools to critically appraise cross‐sectional studies across the application domains outlined and to design and analyze their own cross‐sectional studies that yield high‐quality epidemiologic descriptions.