Analyzing Treatment Effect by Integrating Existing Propensity Score and Outcome Regressions with Heterogeneous Covariate Sets

Author:

Chen Yi-Hau1ORCID,Hsu Szu-Yuan2ORCID,Wang Jie-Huei3ORCID,Su Chien-Chou4

Affiliation:

1. Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan

2. The Third Research Division, Chung-Hua Institution for Economic Research, Taipei 10672, Taiwan

3. Department of Mathematics, National Chung Cheng University, Chiayi 62102, Taiwan

4. Clinical Innovation and Research Center, National Cheng Kung University Hospital, Tainan 70403, Taiwan

Abstract

Analyzing treatment or exposure effect is a major research theme in scientific studies. In the current big-data era where multiple sources of data are available, it is of interest to perform a synthesized analysis of treatment effects by integrating information from different data sources or studies. However, studies may contain heterogeneous and incomplete covariate sets, and individual data therein may not be accessible. We apply and extend the generalized meta-analysis method to integrate summary results (e.g., regression coefficients) of outcome and treatment (propensity score, PS) regression analyses across different datasets that may contain heterogeneous covariate sets. The proposed integrated analysis utilizes a reference dataset, which contains data on the complete set of covariates. The asymptotic distribution for the proposed integrated estimator is established. Simulations reveal that the proposed estimator performs well. We apply the proposed method to obtain the causal effect of waist circumference on hypertension by integrating two existing outcomes and PS regression analyses with different sets of covariates.

Funder

National Science and Technology Council

Publisher

MDPI AG

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