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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.
- 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 âtreatment riskâ 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]
- Subjects :
- DIRICHLET principle
CLINICAL trials
ESTIMATION theory
Subjects
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