Work

A Sequential Significance Test for Treatment by Covariate Interactions

Year: 2019

Type: article

Abstract: bootstrap that can be used to sequentially identify variables that interact with a treatment. We study the theoretical properties of the method, and use simulations to show that it outperforms competing methods in terms of controlling the type-I error rate and achieving satisfactory power. The usefulness of the proposed method is illustrated using real-data examples, from a randomized trial and an observational study.

Source: Statistica Sinica

DOI: 10.5705/ss.202018.0451

Authors: Min Qian, Bibhas Chakraborty, Raju Maiti, Ying Kuen Cheung

Institutions: Columbia University, National University of Singapore, Duke University

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Ministry of Education, India, National Institutes of Health, National Institute of Mental Health, National Institute of General Medical Sciences

Awards: Building Multistage Treatment Regimens for Depression after Acute Coronary SyndromeSyndrome, Novel Methods for Evaluation and Implementation of Behavioral Intervention Technologies for Depression, Post-selection inference and trajectory analysis