Evaluation of multi-outcome longitudinal studies
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Evaluation of multi-outcome longitudinal studies. / Jensen, Signe Marie; Pipper, Christian Bressen; Ritz, Christian.
I: Statistics in Medicine, 2015, s. 1993-2003.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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 -
ID: 132057787