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

Author

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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)

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.

Suggested Citation

  • Yi-Hau Chen & Szu-Yuan Hsu & Jie-Huei Wang & Chien-Chou Su, 2024. "Analyzing Treatment Effect by Integrating Existing Propensity Score and Outcome Regressions with Heterogeneous Covariate Sets," Mathematics, MDPI, vol. 12(14), pages 1-17, July.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2265-:d:1439033
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. S Yang & P Ding, 2018. "Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores," Biometrika, Biometrika Trust, vol. 105(2), pages 487-493.
    3. Jingyu Liang & Jie Liu, 2022. "Evaluation of Educational Interventions Based on Average Treatment Effect: A Case Study," Mathematics, MDPI, vol. 10(22), pages 1-18, November.
    4. Susan Athey & Guido Imbens & Thai Pham & Stefan Wager, 2017. "Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges," American Economic Review, American Economic Association, vol. 107(5), pages 278-281, May.
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