A new approach for sizing trials with composite binary endpoints using anticipated marginal values and accounting for the correlation between components

Composite binary endpoints are increasingly used as primary endpoints in clinical trials. When designing a trial, it is crucial to determine the appropriate sample size for testing the statistical differences between treatment groups for the primary endpoint. As shown in this work, when using a comp...

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Bibliographic Details
Published in:Statistics in medicine Vol. 38; no. 11; pp. 1935 - 1956
Main Authors: Bofill Roig, Marta, Gómez Melis, Guadalupe
Format: Journal Article Publication
Language:English
Published: England Wiley Subscription Services, Inc 20.05.2019
Subjects:
62 Statistics
62D05 Sampling theory, sample surveys
68 Computer science
68U Computing methodologies and applications
92 Biology and other natural sciences
92B Mathematical biology in general
Biomatemàtica
Biomathematics
Classificació AMS
Clinical trials
composite binary endpoints
Computing Methodologies
correlated endpoints
Estadística aplicada
Estadística biosanitària
Estadística matemàtica
Informàtica
Matemàtica aplicada a les ciències
Matemàtiques i estadística
Mostreig (Estadística)
Mètodes estadístics
Research methodology
Sample size
Sampling (Statistics)
Àrees temàtiques de la UPC
composite binary endpoints
correlated endpoints
sample size
ISSN:0277-6715, 1097-0258, 1097-0258
Online Access:Get full text
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Summary:Composite binary endpoints are increasingly used as primary endpoints in clinical trials. When designing a trial, it is crucial to determine the appropriate sample size for testing the statistical differences between treatment groups for the primary endpoint. As shown in this work, when using a composite binary endpoint to size a trial, one needs to specify the event rates and the effect sizes of the composite components as well as the correlation between them. In practice, the marginal parameters of the components can be obtained from previous studies or pilot trials; however, the correlation is often not previously reported and thus usually unknown. We first show that the sample size for composite binary endpoints is strongly dependent on the correlation and, second, that slight deviations in the prior information on the marginal parameters may result in underpowered trials for achieving the study objectives at a pre‐specified significance level. We propose a general strategy for calculating the required sample size when the correlation is not specified and accounting for uncertainty in the marginal parameter values. We present the web platform CompARE to characterize composite endpoints and to calculate the sample size just as we propose in this paper. We evaluate the performance of the proposal with a simulation study and illustrate it by means of a real case study using CompARE.
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ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.8092