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Estimating Treatment Effect Heterogeneity for Binary Outcomes via Dirichlet Multinomial Constraints.

Estimating Treatment Effect Heterogeneity for Binary Outcomes via Dirichlet Multinomial Constraints.

Authors :
Edward J. Mascha
Jeffrey M. Albert
Source :
Biometrical Journal; Jun2007, Vol. 49 Issue 3, p378-393, 16p
Publication Year :
2007

Abstract

In a randomized two-group parallel trial the mean causal effect is typically estimated as the difference in means or proportions for patients receiving, say, either treatment (T) or control (C). Treatment effect heterogeneity (TEH), or unit-treatment interaction, the variability of the causal effect (defined in terms of potential outcomes) across individuals, is often ignored. Since only one of the outcomes, either YT or YC, is observed for each unit in such studies, the TEH is not directly estimable. For convenience, it is often assumed to be minimal or zero. We are particularly interested in the ‘treatment risk’ for binary outcomes, that is, the proportion of individuals who would succeed on C but fail on T. Previous work has shown that the treatment risk can be bounded (Albert, Gadbury and Mascha, 2005), and that the confidence interval width around it can be narrowed using clustered or correlated data (Mascha and Albert, 2006). Without further parameter constraints, treatment risk is unidentifiable. We show, however, that the treatment risk can be directly estimated when the four underlying population counts comprising the joint distribution of the potential outcomes, YT and YC, follow constraints consistent with the Dirichlet multinomial. We propose a test of zero treatment risk and show it to have good size and power. Methods are applied to both a randomized as well as a non-randomized study. Implications for medical decision-making at the policy and individual levels are discussed. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03233847
Volume :
49
Issue :
3
Database :
Complementary Index
Journal :
Biometrical Journal
Publication Type :
Academic Journal
Accession number :
25509303
Full Text :
https://doi.org/10.1002/bimj.200610279