Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?
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[1] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[2] Romain Neugebauer,et al. High‐dimensional propensity score algorithm in comparative effectiveness research with time‐varying interventions , 2015, Statistics in medicine.
[3] J. Avorn,et al. Variable selection for propensity score models. , 2006, American journal of epidemiology.
[4] J. B. Layton,et al. Propensity Score Methods for Confounding Control in Nonexperimental Research , 2013, Circulation. Cardiovascular quality and outcomes.
[5] J. Kaufman. Marginalia: comparing adjusted effect measures. , 2010, Epidemiology.
[6] John M Brooks,et al. Squeezing the balloon: propensity scores and unmeasured covariate balance. , 2013, Health services research.
[7] P. Gustafson,et al. Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies , 2018, Statistical methods in medical research.
[8] Til Stürmer,et al. Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. , 2005, American journal of epidemiology.
[9] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[10] Sebastian Schneeweiss,et al. Variable Selection for Confounding Adjustment in High-dimensional Covariate Spaces When Analyzing Healthcare Databases , 2017, Epidemiology.
[11] Cheng Ju,et al. Propensity score prediction for electronic healthcare databases using super learner and high-dimensional propensity score methods , 2017, Journal of applied statistics.
[12] Mohammad Ehsanul Karim,et al. Estimating inverse probability weights using super learner when weight‐model specification is unknown in a marginal structural Cox model context , 2017, Statistics in medicine.
[13] J. Myers,et al. Effects of adjusting for instrumental variables on bias and precision of effect estimates. , 2011, American journal of epidemiology.
[14] M Alan Brookhart,et al. Covariate selection in high-dimensional propensity score analyses of treatment effects in small samples. , 2011, American journal of epidemiology.
[15] J. Avorn,et al. High-dimensional Propensity Score Adjustment in Studies of Treatment Effects Using Health Care Claims Data , 2009, Epidemiology.
[16] Susan Gruber,et al. Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets , 2015, Statistics in medicine.
[17] S. Rose. Mortality risk score prediction in an elderly population using machine learning. , 2013, American journal of epidemiology.
[18] P. Gustafson,et al. A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding , 2017, Statistics in medicine.
[19] Elizabeth A Stuart,et al. Improving propensity score weighting using machine learning , 2010, Statistics in medicine.
[20] Peter C Austin,et al. Comparing the performance of propensity score methods in healthcare database studies with rare outcomes , 2017, Statistics in medicine.
[21] Cheng Ju,et al. Using Super Learner Prediction Modeling to Improve High-dimensional Propensity Score Estimation , 2018, Epidemiology.
[22] J. Robins,et al. Sensitivity Analyses for Unmeasured Confounding Assuming a Marginal Structural Model for Repeated Measures , 2022 .
[23] Jacques LeLorier,et al. Head to head comparison of the propensity score and the high-dimensional propensity score matching methods , 2016, BMC Medical Research Methodology.
[24] Robert W. Platt,et al. On the role of marginal confounder prevalence – implications for the high‐dimensional propensity score algorithm , 2015, Pharmacoepidemiology and drug safety.
[25] Sebastian Schneeweiss,et al. Comparison of different approaches to confounding adjustment in a study on the association of antipsychotic medication with mortality in older nursing home patients. , 2011, American journal of epidemiology.
[26] Sander Greenland,et al. Invited commentary: variable selection versus shrinkage in the control of multiple confounders. , 2007, American journal of epidemiology.
[27] Sebastian Schneeweiss,et al. Using high‐dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system , 2012, Pharmacoepidemiology and drug safety.
[28] M. J. van der Laan. Targeted Maximum Likelihood Based Causal Inference: Part I , 2010, The international journal of biostatistics.
[29] Robert W. Platt,et al. Targeted Maximum Likelihood Estimation for Pharmacoepidemiologic Research , 2016, Epidemiology.
[30] J. Pearl. Invited commentary: understanding bias amplification. , 2011, American journal of epidemiology.
[31] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[32] R. Tibshirani. The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.
[33] Jennifer M. Polinski,et al. Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases , 2014, Comput. Stat. Data Anal..
[34] Paul Gustafson,et al. Hypothesis Testing for an Exposure–Disease Association in Case–Control Studies Under Nondifferential Exposure Misclassification in the Presence of Validation Data: Bayesian and Frequentist Adjustments , 2016 .
[35] P. Gustafson,et al. Practice of Epidemiology Marginal Structural Cox Models for Estimating the Association Between β-Interferon Exposure and Disease Progression in a Multiple Sclerosis Cohort , 2014 .
[36] James M Robins,et al. Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. , 2016, American journal of epidemiology.
[37] Sengwee Toh,et al. Confounding adjustment via a semi‐automated high‐dimensional propensity score algorithm: an application to electronic medical records , 2011, Pharmacoepidemiology and drug safety.
[38] M. E. Karim. Can joint replacement reduce cardiovascular risk? , 2013, BMJ.
[39] I. Bross. Spurious effects from an extraneous variable. , 1966, Journal of chronic diseases.
[40] T. Brennan,et al. Observing versus Predicting: Initial Patterns of Filling Predict Long-Term Adherence More Accurately Than High-Dimensional Modeling Techniques. , 2016, Health services research.
[41] S Greenland,et al. The effect of misclassification in the presence of covariates. , 1980, American journal of epidemiology.
[42] Sebastian Schneeweiss,et al. Regularized Regression Versus the High-Dimensional Propensity Score for Confounding Adjustment in Secondary Database Analyses. , 2015, American journal of epidemiology.
[43] M. J. van der Laan,et al. Practice of Epidemiology Improving Propensity Score Estimators ’ Robustness to Model Misspecification Using Super Learner , 2015 .
[44] E. Garbe,et al. The Potential of High‐Dimensional Propensity Scores in Health Services Research: An Exemplary Study on the Quality of Care for Elective Percutaneous Coronary Interventions , 2018, Health services research.
[45] Helen Tremlett,et al. On the application of statistical learning approaches to construct inverse probability weights in marginal structural Cox models: Hedging against weight-model misspecification , 2017, Commun. Stat. Simul. Comput..