Format

Send to

Choose Destination
Biometrics. 2017 Dec;73(4):1199-1209. doi: 10.1111/biom.12676. Epub 2017 Feb 17.

A general statistical framework for subgroup identification and comparative treatment scoring.

Author information

1
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53792, U.S.A.
2
Department of Biomedical Data Science, Stanford University, Palo Alto, California 94305, U.S.A.
3
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A.

Abstract

Many statistical methods have recently been developed for identifying subgroups of patients who may benefit from different available treatments. Compared with the traditional outcome-modeling approaches, these methods focus on modeling interactions between the treatments and covariates while by-pass or minimize modeling the main effects of covariates because the subgroup identification only depends on the sign of the interaction. However, these methods are scattered and often narrow in scope. In this article, we propose a general framework, by weighting and A-learning, for subgroup identification in both randomized clinical trials and observational studies. Our framework involves minimum modeling for the relationship between the outcome and covariates pertinent to the subgroup identification. Under the proposed framework, we may also estimate the magnitude of the interaction, which leads to the construction of scoring system measuring the individualized treatment effect. The proposed methods are quite flexible and include many recently proposed estimators as special cases. As a result, some estimators originally proposed for randomized clinical trials can be extended to observational studies, and procedures based on the weighting method can be converted to an A-learning method and vice versa. Our approaches also allow straightforward incorporation of regularization methods for high-dimensional data, as well as possible efficiency augmentation and generalization to multiple treatments. We examine the empirical performance of several procedures belonging to the proposed framework through extensive numerical studies.

KEYWORDS:

A-learning; Individualized treatment rules; Observational studies; Propensity score; Regularization

PMID:
28211943
PMCID:
PMC5561419
DOI:
10.1111/biom.12676
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Wiley Icon for PubMed Central
Loading ...
Support Center