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PMC10 COMPARISON BETWEEN PROPENSITY SCORE AND TRADITIONAL COVARIATE ADJUSTMENT WITH LOGISTIC REGRESSION MODELS IN ESTIMATING THE AVERAGE TREATMENT EFFECTS (ATES): RESULTS FROM MONTE CARLO SIMULATIONS

机译：在评估平均处理效果（日期）时，倾向评分和传统变量调整与Logistic回归模型之间的比较：Monte Carlo模拟的结果

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Le QA; Hay JW;
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2. The role of prediction modeling in propensity score estimation: An evaluation of logistic regression, bcart, and the covariate-balancing propensity score [J] . WyssR., EllisA.R., BrookhartM.A., American Journal of Epidemiology . 2014,第6期

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3. The Comparison of Latent Variable Propensity Score Models to Traditional Propensity Score Models under Conditions of Covariate Unreliability [J] . Whittaker Tiffany A. Multivariate behavioral research . 2020,第4期

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4. Bayesian Estimation of Spatio-Temporal Models with Covariates Measured with Spatio-Temporally Correlated Errors: Evidence from Monte Carlo Simulation [C] . Mohammad Masjkur, Henk Folmer Bandung Creative Movement International Conference on Creative Industries . 2018

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5. Resampling approach for estimating prediction error and for adjusting logistic regression models for covariate measurement error. [D] . Li, Wei. 2002

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6. The Role of Prediction Modeling in Propensity Score Estimation: An Evaluation of Logistic Regression bCART and the Covariate-Balancing Propensity Score [O] . Richard Wyss, Alan R. Ellis, M. Alan Brookhart, -1

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PMC10 COMPARISON BETWEEN PROPENSITY SCORE AND TRADITIONAL COVARIATE ADJUSTMENT WITH LOGISTIC REGRESSION MODELS IN ESTIMATING THE AVERAGE TREATMENT EFFECTS (ATES): RESULTS FROM MONTE CARLO SIMULATIONS

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