Observational Study Designs

Written and curated by Emily Potts, MS, Jiying Han, MS, and Pam Factor-Litvak, PhD.

Overview

Observational study designs are essential for exploring associations and trends between variables without direct intervention on the exposure of interest. Such designs are necessary when direct manipulation of exposures is either impossible or unethical (e.g., assigning harmful treatments to participants). Common types of observational studies include cross-sectional, cohort, and case-control studies. Cross-sectional studies offer a snapshot of data at a single time point, showing the distribution of and relationships between characteristics and outcomes in a given population. Cohort studies follow individuals over time (either prospectively or retrospectively) showing associations between exposures measured earlier in time and outcome variables measured later; these studies allow one to assume temporality in the associations. Finally, case-control studies are a useful retrospective design that allows for the study of rare outcomes. A key limitation of observational studies is their inability to establish causality definitively, although causal inference methods seek to address this gap. Moreover, observational studies face confounding, selection bias, information bias, or difficulty in controlling for external factors, which can impact the generalizability of their findings. Despite these limitations, observational designs are more feasible and ethical when manipulation is not possible, and advanced causal inference methods like propensity score matching and instrumental variable analysis can help address confounding and bias, improving the validity of findings. Observational studies play a crucial role in understanding human behavior, health, and disease trends and are commonly done.