Planning and evaluating clinical trials with composite time-to-first-event endpoints in a competing risk framework
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Planning and evaluating clinical trials with composite time-to-first-event endpoints in a competing risk framework. / Rauch, G; Beyersmann, J.
in: STAT MED, Jahrgang 32, Nr. 21, 20.09.2013, S. 3595-3608.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Planning and evaluating clinical trials with composite time-to-first-event endpoints in a competing risk framework
AU - Rauch, G
AU - Beyersmann, J
N1 - Copyright © 2013 John Wiley & Sons, Ltd.
PY - 2013/9/20
Y1 - 2013/9/20
N2 - Composite endpoints combine several events of interest within a single variable. These are often time-to-first-event data, which are analyzed via survival analysis techniques. To demonstrate the significance of an overall clinical benefit, it is sufficient to assess the test problem formulated for the composite. However, the effect observed for the composite does not necessarily reflect the effects for the components. Therefore, it would be desirable that the sample size for clinical trials using composite endpoints provides enough power not only to detect a clinically relevant superiority for the composite but also to address the components in an adequate way. The single components of a composite endpoint assessed as time-to-first-event define competing risks. We consider multiple test problems based on the cause-specific hazards of competing events to address the problem of analyzing both a composite endpoint and its components. Thereby, we use sequentially rejective test procedures to reduce the power loss to a minimum. We show how to calculate the sample size for the given multiple test problem by using a simply applicable simulation tool in SAS. Our ideas are illustrated by two clinical study examples.
AB - Composite endpoints combine several events of interest within a single variable. These are often time-to-first-event data, which are analyzed via survival analysis techniques. To demonstrate the significance of an overall clinical benefit, it is sufficient to assess the test problem formulated for the composite. However, the effect observed for the composite does not necessarily reflect the effects for the components. Therefore, it would be desirable that the sample size for clinical trials using composite endpoints provides enough power not only to detect a clinically relevant superiority for the composite but also to address the components in an adequate way. The single components of a composite endpoint assessed as time-to-first-event define competing risks. We consider multiple test problems based on the cause-specific hazards of competing events to address the problem of analyzing both a composite endpoint and its components. Thereby, we use sequentially rejective test procedures to reduce the power loss to a minimum. We show how to calculate the sample size for the given multiple test problem by using a simply applicable simulation tool in SAS. Our ideas are illustrated by two clinical study examples.
KW - Angiotensin II Type 1 Receptor Blockers
KW - Atherosclerosis
KW - Clinical Trials as Topic
KW - Computer Simulation
KW - Diabetes Mellitus, Type 2
KW - Diabetic Nephropathies
KW - Endpoint Determination
KW - Humans
KW - Losartan
KW - Research Design
KW - Sample Size
KW - Survival Analysis
KW - Thrombin
KW - Journal Article
KW - Research Support, Non-U.S. Gov't
U2 - 10.1002/sim.5798
DO - 10.1002/sim.5798
M3 - SCORING: Journal article
C2 - 23553898
VL - 32
SP - 3595
EP - 3608
JO - STAT MED
JF - STAT MED
SN - 0277-6715
IS - 21
ER -
