Journal article
A rank-based approach to estimating monotone individualized two treatment regimes
Computational statistics & data analysis, Vol.151, p.107015
11/01/2020
DOI: 10.1016/j.csda.2020.107015
Abstract
Developing effective individualized treatment rules (ITRs) for diseases is an important goal of clinical research. Much effort has been devoted to estimating individualized treatment effects in the recent literature. However, there have not been systematic studies on the robust inference for individualized treatment effects when there exist potential outliers. We propose a monotone ITR in the framework of a semiparametric generalized regression with two treatments and estimate the treatment effects via a smoothed maximum rank correlation procedure. We provide sufficient conditions under which the proposed estimator has an asymptotically normal distribution whose variance can be consistently estimated based on a resampling procedure. We evaluate the finite-sample properties of our proposed approach via simulation studies. We also illustrate the proposed method by applying it to a data set from an AIDS clinical trials study. (C) 2020 Elsevier B.V. All rights reserved.
Details
- Title: Subtitle
- A rank-based approach to estimating monotone individualized two treatment regimes
- Creators
- Haixiang Zhang - Tianjin UniversityJian Huang - University of IowaLiuquan Sun - Chinese Academy of Sciences
- Resource Type
- Journal article
- Publication Details
- Computational statistics & data analysis, Vol.151, p.107015
- Publisher
- ELSEVIER
- DOI
- 10.1016/j.csda.2020.107015
- ISSN
- 0167-9473
- eISSN
- 1872-7352
- Number of pages
- 12
- Grant note
- 11771431; 11690015; 11926341 / National Natural Science Foundation of China 2008DP173182 / Key Laboratory of RCSDS, CAS, China DMS-1916199 / National Science Foundation of USA
- Language
- English
- Date published
- 11/01/2020
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257609302771
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