title: Evaluation of multi-outcome longitudinal studies.
abstract: Evaluation of intervention effects on multiple outcomes is a common scenario in clinical studies. In longitudinal studies, such evaluation is a challenge if one wishes to adequately capture simultaneous data behavior. In this situation, a common approach is to analyze each outcome separately. As a result, multiple statistical statements describing the intervention effect need to be reported and an adjustment for multiple testing is necessary. This is typically done by means of the Bonferroni procedure, which does not take into account the correlation between outcomes, thus resulting in overly conservative conclusions. We propose an alternative approach for multiplicity adjustment that incorporates dependence between outcomes, resulting in an appreciably less conservative evaluation. The ability of the proposed method to control the familywise error rate is evaluated in a simulation study, and the applicability of the method is demonstrated in two examples from the literature.
authors.affiliation: Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 30, 1958, Frederiksberg, DK.
authors.forename: Signe M
authors.initials: SM
authors.lastname: Jensen
authors.forename: Christian B
authors.initials: CB
authors.lastname: Pipper
authors.forename: Christian
authors.initials: C
authors.lastname: Ritz
mesh_terms: D000293:Adolescent; D000704:Analysis of Variance; D015982:Bias; D002648:Child; D003198:Computer Simulation; D018592:Cross-Over Studies; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008297:Male; D024821:Metabolic Syndrome / Q000378:metabolism; D008894:Milk Proteins / Q000378:metabolism; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data*; D050177:Overweight / Q000378:metabolism; D016032:Randomized Controlled Trials as Topic; D012307:Risk Factors; D012528:Satiety Response / Q000502:physiology; D055815:Young Adult
chemical_list: D008894:Milk Proteins
keywords: asymptotic representation; intervention studies; linear mixed models; multiple testing; type I error