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Use of copula to model within‐study association in bivariate meta‐analysis of binomial data at the aggregate level : a Bayesian approach and application to surrogate endpoint evaluation
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Papanikos, Tasos, Thompson, John R., Abrams, Keith R. and Bujkiewicz, Sylwia (2022) Use of copula to model within‐study association in bivariate meta‐analysis of binomial data at the aggregate level : a Bayesian approach and application to surrogate endpoint evaluation. Statistics in Medicine, 41 (25). pp. 4961-4981. doi:10.1002/sim.9547 ISSN 1097-0258.
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Official URL: https://doi.org/10.1002/sim.9547
Abstract
Bivariate meta‐analysis provides a useful framework for combining information across related studies and has been utilized to combine evidence from clinical studies to evaluate treatment efficacy on two outcomes. It has also been used to investigate surrogacy patterns between treatment effects on the surrogate endpoint and the final outcome. Surrogate endpoints play an important role in drug development when they can be used to measure treatment effect early compared to the final outcome and to predict clinical benefit or harm. The standard bivariate meta‐analytic approach models the observed treatment effects on the surrogate and the final outcome outcomes jointly, at both the within‐study and between‐studies levels, using a bivariate normal distribution. For binomial data, a normal approximation on log odds ratio scale can be used. However, this method may lead to biased results when the proportions of events are close to one or zero, affecting the validation of surrogate endpoints. In this article, we explore modeling the two outcomes on the original binomial scale. First, we present a method that uses independent binomial likelihoods to model the within‐study variability avoiding to approximate the observed treatment effects. However, the method ignores the within‐study association. To overcome this issue, we propose a method using a bivariate copula with binomial marginals, which allows the model to account for the within‐study association. We applied the methods to an illustrative example in chronic myeloid leukemia to investigate the surrogate relationship between complete cytogenetic response and event‐free‐survival.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics R Medicine > R Medicine (General) R Medicine > RM Therapeutics. Pharmacology |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Library of Congress Subject Headings (LCSH): | Copulas (Mathematical statistics), Meta-analysis, Drugs -- Testing -- Statistical methods, Clinical trials -- Statistical methods | ||||||||
Journal or Publication Title: | Statistics in Medicine | ||||||||
Publisher: | John Wiley & Sons, Inc. | ||||||||
ISSN: | 1097-0258 | ||||||||
Official Date: | 10 November 2022 | ||||||||
Dates: |
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Volume: | 41 | ||||||||
Number: | 25 | ||||||||
Page Range: | pp. 4961-4981 | ||||||||
DOI: | 10.1002/sim.9547 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Copyright Holders: | © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. | ||||||||
Date of first compliant deposit: | 15 February 2023 | ||||||||
Date of first compliant Open Access: | 15 February 2023 | ||||||||
RIOXX Funder/Project Grant: |
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