Simultaneous inference for multiple marginal generalized estimating equation models.

ABSTRACT: Motivated by small-sample studies in ophthalmology and dermatology, we study the problem of simultaneous inference for multiple endpoints in the presence of repeated observations. We propose a framework in which a generalized estimating equation model is fit for each endpoint marginally, taking into account dependencies within the same subject. The asymptotic joint normality of the stacked vector of marginal estimating equations is used to derive Wald-type simultaneous confidence intervals and hypothesis tests for multiple linear contrasts of regression coefficients of the multiple marginal models. The small sample performance of this approach is improved by a bias adjustment to the estimate of the joint covariance matrix of the regression coefficients from multiple models. As a further sm

SUBMITTER: Ristl R 

PROVIDER: S-EPMC7270726 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature