{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T07:05:55Z","timestamp":1791011155145,"version":"4.1.0"},"reference-count":54,"publisher":"Wiley","issue":"14","license":[{"start":{"date-parts":[[2001,6,28]],"date-time":"2001-06-28T00:00:00Z","timestamp":993686400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2001,7,30]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Epidemiologic studies of disease often produce inconclusive or contradictory results due to small sample sizes or regional variations in the disease incidence or the exposures. To clarify these issues, researchers occasionally pool and reanalyse original data from several large studies. In this paper we explore the use of a two\u2010stage random\u2010effects model for analysing pooled case\u2010control studies and undertake a thorough examination of bias in the pooled estimator under various conditions. The two\u2010stage model analyses each study using the model appropriate to the design with study\u2010specific confounders, and combines the individual study\u2010specific adjusted log\u2010odds ratios using a linear mixed\u2010effects model; it is computationally simple and can incorporate study\u2010level covariates and random effects. Simulations indicate that when the individual studies are large, two\u2010stage methods produce nearly unbiased exposure estimates and standard errors of the exposure estimates from a generalized linear mixed model. By contrast, joint fixed\u2010effects logistic regression produces attenuated exposure estimates and underestimates the standard error when heterogeneity is present. While bias in the pooled regression coefficient increases with interstudy heterogeneity for both models, it is much smaller using the two\u2010stage model. In pooled analyses, where covariates may not be uniformly defined and coded across studies, and occasionally not measured in all studies, a joint model is often not feasible. The two\u2010stage method is shown to be a simple, valid and practical method for the analysis of pooled binary data. The results are applied to a study of reproductive history and cutaneous melanoma risk in women using data from ten large case\u2010control studies. 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