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

Yi-Hau Chen (), Szu-Yuan Hsu, Jie-Huei Wang () and Chien-Chou Su
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Yi-Hau Chen: Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan
Szu-Yuan Hsu: The Third Research Division, Chung-Hua Institution for Economic Research, Taipei 10672, Taiwan
Jie-Huei Wang: Department of Mathematics, National Chung Cheng University, Chiayi 62102, Taiwan
Chien-Chou Su: Clinical Innovation and Research Center, National Cheng Kung University Hospital, Tainan 70403, Taiwan

Mathematics, 2024, vol. 12, issue 14, 1-17

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.

Keywords: data integration; multi-center study; missing covariate; treatment effect (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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