title: Simultaneous comparisons of treatments at multiple time points: Combined marginal models versus joint modeling.
abstract: 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.
authors.affiliation: 1 Institute of Biostatistics, Leibniz University Hannover, Hannover, Germany.
authors.forename: Philip
authors.initials: P
authors.lastname: Pallmann
authors.affiliation: 2 Department of Anesthesiology, Vanderbilt University Medical School, Nashville, TN, USA.
authors.forename: Mias
authors.initials: M
authors.lastname: Pretorius
authors.affiliation: 3 Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark.
authors.forename: Christian
authors.initials: C
authors.lastname: Ritz
mesh_terms: D056808:Biostatistics / Q000379:methods*; D065168:Bradykinin Receptor Antagonists / Q000494:pharmacology; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D005338:Fibrin Fibrinogen Degradation Products / Q000378:metabolism; D005342:Fibrinolysis / Q000187:drug effects; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D013997:Time Factors
chemical_list: D065168:Bradykinin Receptor Antagonists; D005338:Fibrin Fibrinogen Degradation Products; C036309:fibrin fragment D
keywords: AICc; Longitudinal data; generalized least squares; linear mixed-effects model; repeated measurements