Multicenter Cohort Study

A multicenter cohort study collects individual-level data under a shared prospective protocol, allowing harmonized variables, consistent covariate adjustment, and person-level subgroup analyses. A meta-analysis synthesizes published summary statistics from separately conducted studies that may differ in design, population, and measurement — it cannot re-analyze individual records or adjust for covariates not reported in the original papers. The multicenter design is generally preferred when coordination is feasible because it avoids the ecological fallacy and publication bias that can affect meta-analyses.

In a federated analysis, each site runs identical analysis scripts on its local data and shares only summary statistics (e.g., regression coefficients and standard errors) with the coordinating center, which then combines them using fixed- or random-effects meta-analytic methods. This approach is preferred when data-sharing agreements or privacy legislation prohibit transfer of individual records. It yields statistically comparable results to full pooling for most analyses, though some individual-level interactions cannot be estimated without the merged dataset.

Site should almost always be accounted for explicitly. Common approaches include: stratifying the Cox or Poisson model by site (allows site-specific baseline hazards); including site as a fixed-effect covariate; or treating site as a random effect (mixed model) when the number of sites is large enough to estimate between-site variance reliably. Ignoring site clustering underestimates standard errors and can produce false precision.

Sample size depends on the expected incidence of the outcome, the magnitude of the exposure-outcome association, and the precision required. For rare outcomes (incidence < 1 per 1,000 per year) tens of thousands of person-years may be needed. Formal power calculations should be conducted before study launch, incorporating assumptions about attrition, outcome ascertainment rates, and the intraclass correlation for clustering within sites.

Like all observational designs, a multicenter cohort can establish temporality (exposure precedes outcome) and quantify associations with high precision, but it cannot rule out unmeasured confounding. Causal inference techniques such as propensity score adjustment, instrumental variable analysis, or Mendelian randomization can be applied to multicenter cohort data to strengthen causal arguments, but each requires its own assumptions. A randomized multicenter trial remains the gold standard for causal claims.