Simultaneous inference for multiple marginal generalized estimating equation models

Research output: Contribution to journalJournal article

Robin Ristl, Ludwig Hothorn, Christian Ritz, Martin Posch

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 small sample improvement a multivariate t-distribution with appropriate degrees of freedom is specified as reference distribution. In addition, a generalized score test based on the stacked estimating equations is derived. Simulation results show strong control of the family-wise type I error rate for these methods even with small sample sizes and increased power compared to a Bonferroni-Holm multiplicity adjustment. Thus, the proposed methods are suitable to efficiently use the information from repeated observations of multiple endpoints in small-sample studies.

Original languageEnglish
Article number962280219873005
JournalStatistical Methods in Medical Research
Publication statusE-pub ahead of print - 17 Sep 2019

    Research areas

  • Faculty of Science - Generalized estimating equations, Multiple testing, Multiple endpoints, Dependent observations, Small samples

ID: 227694805