Journal article
Exploration of Heterogeneous Treatment Effects via Concave Fusion
The international journal of biostatistics, Vol.16(1), 20180026
09/20/2019
DOI: 10.1515/ijb-2018-0026
PMID: 31541601
Abstract
Understanding treatment heterogeneity is essential to the development of precision medicine, which seeks to tailor medical treatments to subgroups of patients with similar characteristics. One of the challenges of achieving this goal is that we usually do not have
knowledge of the grouping information of patients with respect to treatment effect. To address this problem, we consider a heterogeneous regression model which allows the coefficients for treatment variables to be subject-dependent with unknown grouping information. We develop a concave fusion penalized method for estimating the grouping structure and the subgroup-specific treatment effects, and derive an alternating direction method of multipliers algorithm for its implementation. We also study the theoretical properties of the proposed method and show that under suitable conditions there exists a local minimizer that equals the oracle least squares estimator based on
knowledge of the true grouping information with high probability. This provides theoretical support for making statistical inference about the subgroup-specific treatment effects using the proposed method. The proposed method is illustrated in simulation studies and illustrated with real data from an AIDS Clinical Trials Group Study.
Details
- Title: Subtitle
- Exploration of Heterogeneous Treatment Effects via Concave Fusion
- Creators
- Shujie Ma - University of California, RiversideJian Huang - Department of Statistics and Actuarial Science, University of Iowa, Iowa City, USAZhiwei Zhang - University of California, RiversideMingming Liu - University of California, Riverside
- Resource Type
- Journal article
- Publication Details
- The international journal of biostatistics, Vol.16(1), 20180026
- Publisher
- De Gruyter
- DOI
- 10.1515/ijb-2018-0026
- PMID
- 31541601
- ISSN
- 2194-573X
- eISSN
- 1557-4679
- Number of pages
- 26
- Language
- English
- Date published
- 09/20/2019
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257744102771
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