A weighted combined effect measure for the analysis of a composite time-to-first-event endpoint with components of different clinical relevance

TY - JOUR

T1 - A weighted combined effect measure for the analysis of a composite time-to-first-event endpoint with components of different clinical relevance

AU - Rauch, Geraldine

AU - Kunzmann, Kevin

AU - Kieser, Meinhard

AU - Wegscheider, Karl

AU - König, Jochem

AU - Eulenburg, Christine

N1 - Copyright © 2017 John Wiley & Sons, Ltd.

PY - 2018/2/28

Y1 - 2018/2/28

N2 - Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all-cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all-cause hazard within each group is given by the sum of the cause-specific hazards corresponding to the individual components. A natural extension of the standard all-cause hazard ratio can be defined by a "weighted all-cause hazard ratio" where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all-cause hazard ratio then corresponds to the standard all-cause hazard ratio. To identify the cause-specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all-cause hazard ratio by theoretical considerations, Monte-Carlo simulations, and by means of a real clinical trial example.

AB - Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all-cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all-cause hazard within each group is given by the sum of the cause-specific hazards corresponding to the individual components. A natural extension of the standard all-cause hazard ratio can be defined by a "weighted all-cause hazard ratio" where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all-cause hazard ratio then corresponds to the standard all-cause hazard ratio. To identify the cause-specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all-cause hazard ratio by theoretical considerations, Monte-Carlo simulations, and by means of a real clinical trial example.

KW - Journal Article

U2 - 10.1002/sim.7531

DO - 10.1002/sim.7531

M3 - SCORING: Journal article

C2 - 29205425

VL - 37

SP - 749

EP - 767

JO - STAT MED

JF - STAT MED

SN - 0277-6715

IS - 5

ER -