A general statistical framework for subgroup identification and comparative treatment scoring

Shuai Chen, Lu Tian, Tianxi Cai, Menggang Yu

Research output: Contribution to journalArticle

13 Citations (Scopus)

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.

Original languageEnglish (US)
JournalBiometrics
DOIs
StateAccepted/In press - Jan 1 2017
Externally publishedYes

Fingerprint

Scoring
Statistical methods
Subgroup
Covariates
Randomized Clinical Trial
Observational Study
randomized clinical trials
Modeling
Weighting
observational studies
Interaction
Therapeutics
Estimator
learning
methodology
Main Effect
Augmentation
Treatment Effects
Regularization Method
High-dimensional Data

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

A general statistical framework for subgroup identification and comparative treatment scoring. / Chen, Shuai; Tian, Lu; Cai, Tianxi; Yu, Menggang.

In: Biometrics, 01.01.2017.

Research output: Contribution to journalArticle

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