TY - JOUR

T1 - Evaluation of multi-outcome longitudinal studies

AU - Jensen, Signe Marie

AU - Pipper, Christian Bressen

AU - Ritz, Christian

N1 - CURIS 2015 NEXS 085

PY - 2015

Y1 - 2015

N2 - 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. Copyright © 2015 John Wiley & Sons, Ltd.

AB - 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. Copyright © 2015 John Wiley & Sons, Ltd.

U2 - 10.1002/sim.6461

DO - 10.1002/sim.6461

M3 - Journal article

C2 - 25720498

SP - 1993

EP - 2003

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

ER -