Simultaneous comparisons of treatments at multiple time points: Combined marginal models versus joint modeling

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Philip Pallmann, Mias Pretorius, Christian Ritz

We discuss several aspects of multiple inference in longitudinal settings, focusing on many-to-one and all-pairwise comparisons of (a) treatment groups simultaneously at several points in time, or (b) time points simultaneously for several treatments. We assume a continuous endpoint that is measured repeatedly over time and contrast two basic modeling strategies: fitting a joint model across all occasions (with random effects and/or some residual covariance structure to account for heteroscedasticity and serial dependence), and a novel approach combining a set of simple marginal, i.e. occasion-specific models. Upon parameter and covariance estimation with either modeling approach, we employ a variant of multiple contrast tests that acknowledges correlation between time points and test statistics. This method provides simultaneous confidence intervals and adjusted p-values for elementary hypotheses as well as a global test decision. We compare via simulation the powers of multiple contrast tests based on a joint model and multiple marginal models, respectively, and quantify the benefit of incorporating longitudinal correlation, i.e. the advantage over Bonferroni. Practical application is illustrated with data from a clinical trial on bradykinin receptor antagonism.

OriginalsprogEngelsk
TidsskriftStatistical Methods in Medical Research
Vol/bind26
Udgave nummer6
Sider (fra-til)2633-2648
Antal sider16
ISSN0962-2802
DOI
StatusUdgivet - 2017

Bibliografisk note

CURIS 2017 NEXS 362

ID: 144414715