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For my publication list, click here. The following list is created for the beginners who might be interested in learning about High-dimensional Propensity score algorithm.


  • S. Schneeweiss, J.A. Rassen, R.J. Glynn, J. Avorn, H. Mogun, and M.A. Brookhart. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology (Cambridge, Mass.), 20(4):512, 2009.
  • J.A. Rassen, R.J. Glynn, M.A. Brookhart, and S. Schneeweiss. Covariate selection in high-dimensional propensity score analyses of treatment effects in small samples. American journal of epidemiology, 173(12):1404–1413, 2011.
  • J.A. Rassen and S. Schneeweiss. Using high-dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system. Pharmacoepidemiology and drug safety, 21(S1):41–49, 2012.
  • J.M. Franklin, S. Schneeweiss, J.M. Polinski, and J.A. Rassen. Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases. Computational statistics & data analysis, 72:219–226, 2014.
  • M. Pang, T. Schuster, K.B. Filion, M.E. Schnitzer, M. Eberg, and R.W. Platt. Effect estimation in point-exposure studies with binary outcomes and high-dimensional covariate data - a comparison of targeted maximum likelihood estimation and inverse probability of treatment weighting. Journal, in press, 2015.
  • Menglan Pang, Tibor Schuster, Kristian B. Filion, Maria Eberg, and Robert W. Platt. Targeted maximum likelihood estimation for pharmacoepidemiological research: A real-world example using the clinical practice research datalink. Epidemiology, 27(4):570–577, 2015.
  • T. Schuster, M. Pang, and R.W. Platt. On the role of marginal confounder prevalence–implications for the high-dimensional propensity score algorithm. Pharmacoepidemiology and drug safety, 24(9):1004–1007, 2015.
  • Dirk Enders, Christoph Ohlmeier, and Edeltraut Garbe. The potential of high-dimensional propensity scores in health services research: An exemplary study on the quality of care for elective percutaneous coronary interventions. Health Services Research, pages 1–17, 2017.
  • J.R. Guertin, E. Rahme, C.R. Dormuth, and J. LeLorier. Head to head comparison of the propensity score and the high-dimensional propensity score matching methods. BMC medical research methodology, 16(1):1–10, 2016.
  • S. Toh, L.A. García Rodríguez, and M.A. Hernán. Confounding adjustment via a semi-automated high-dimensional propensity score algorithm: an application to electronic medical records. Pharmacoepidemiology and drug safety, 20(8):849–857, 2011.
  • Sebastian Schneeweiss, Wesley Eddings, Robert J Glynn, Elisabetta Patorno, Jeremy Rassen, and Jessica M Franklin. Variable selection for confounding adjustment in high-dimensional covariate spaces when analyzing healthcare databases. Epidemiology, 2017.
  • Jessica M Franklin, Wesley Eddings, Peter C Austin, Elizabeth A Stuart, and Sebastian Schneeweiss. Comparing the performance of propensity score methods in healthcare database studies with rare outcomes. Statistics in Medicine, 2017.
  • C. Ju, M. Combs, S.D. Lendle, J.M. Franklin, R. Wyss, S. Schneeweiss, and M.J. van der Laan. Propensity score prediction for electronic healthcare dataset using super learner and high-dimensional propensity score method. Website, 2016. Last accessed: July 27. URL:
  • J.M. Franklin, W.H. Shrank, J. Lii, A.K. Krumme, O.S. Matlin, T.A. Brennan, and N.K. Choudhry. Observing versus predicting: Initial patterns of filling predict long-term adherence more accurately than high-dimensional modeling techniques. Health services research, 51(1):220–239, 2016.
  • J.M. Franklin, W. Eddings, R.J. Glynn, and S. Schneeweiss. Regularized regression versus the high-dimensional propensity score for confounding adjustment in secondary database analyses. American journal of epidemiology, 182(7):651–659, 2015.

Machine learning in high dimensions or propensity score context

  • Danning He, Simon C Mathews, Anthony N Kalloo, and Susan Hutfless. Mining high-dimensional administrative claims data to predict early hospital readmissions. Journal of the American Medical Informatics Association, 21(2):272–279, 2014.
  • Miguel A Hernán and James M Robins. Using big data to emulate a target trial when a randomized trial is not available. American journal of epidemiology, 183(8):758–764, 2016.
  • P. Thottakkara, T. Ozrazgat-Baslanti, B.B. Hupf, P. Rashidi, P. Pardalos, P. Momcilovic, and A. Bihorac. Application of machine learning techniques to high-dimensional clinical data to forecast postoperative complications. PloS one, 11(5):e0155705, 2016.
  • D. Westreich, J. Lessler, and M.J. Funk. Propensity score estimation: machine learning and classification methods as alternatives to logistic regression. Journal of Clinical Epidemiology, 63(8):826, 2010.
  • S. Rose. Mortality risk score prediction in an elderly population using machine learning. American journal of epidemiology, 177(5):443–452, 2013.
  • R. Pirracchio, M.L. Petersen, and M. van der Laan. Improving propensity score estimators’ robustness to model misspecification using super learner. American journal of epidemiology, 181(2):108–119, 2015.
  • B.K. Lee, J. Lessler, and E.A. Stuart. Improving propensity score weighting using machine learning. Statistics in Medicine, 29(3):337–346, 2010.
  • D.F. McCaffrey, G. Ridgeway, and A.R. Morral. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9(4):403–425, 2004.
  • Mohammad Ehsanul Karim, John Petkau, Paul Gustafson, and Helen Tremlett. On the application of statistical learning approaches to construct inverse probability weights in marginal structural cox models: Hedging against weight-model misspecification, 2016. DOI: 10.1080/03610918.2016.1248574, published online: 21 Oct 2016.
  • Mohammad Ehsanul Karim and Robert W Platt. Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural cox model context, 2017. DOI:10.1002/sim.7266, published online 20 Feb 2017.
  • B.S. Keller, J. Kim, and P.M. Steiner. Data mining alternatives to logistic regression for propensity score estimation: Neural networks and support vector machines. Multivariate Behavioral Research, 48(1):164–164, 2013.
  • S. Watkins, M. Jonsson-Funk, M.A. Brookhart, S.A. Rosenberg, T.M. O’Shea, and J. Daniels. An empirical comparison of tree-based methods for propensity score estimation. Health Services Research, 48(5):1798–1817, 2013.
  • S. Gruber, R.W. Logan, I. Jarrín, S. Monge, and M.A. Hernán. Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets. Statistics in Medicine, 2014.
  • R. Neugebauer, J.A. Schmittdiel, Z. Zhu, J.A. Rassen, J.D. Seeger, and S. Schneeweiss. High-dimensional propensity score algorithm in comparative effectiveness research with time-varying interventions. Statistics in medicine, 34(5):753–781, 2014.

Unmeasured confounding and misclassification

  • I.D.J. Bross. Spurious effects from an extraneous variable. Journal of chronic diseases, 19(6):637–647, 1966.
  • Timothy L Lash, Aliza K Fink, and Matthew P Fox. Unmeasured and unknown confounders. In Applying Quantitative Bias Analysis to Epidemiologic Data, pages 59–78. Springer, 2009.
  • B.A. Brumback, M.A. Hernán, S.J.P.A. Haneuse, and J.M. Robins. Sensitivity analyses for unmeasured confounding assuming a marginal structural model for repeated measures. Statistics in Medicine, 23(5):749–767, 2004.
  • L.C. McCandless, P. Gustafson, and A. Levy. Bayesian sensitivity analysis for unmeasured confounding in observational studies. Statistics in medicine, 26(11):2331–2347, 2007.
  • Jessica Kasza, Rory Wolfe, and Tibor Schuster. Assessing the impact of unmeasured confounding for binary outcomes using confounding functions., 2017. doi: 10.1093/ije/dyx023.
  • Sander Greenland. The effect of misclassification in the presence of covariates. American journal of epidemiology, 112(4):564–569, 1980.
  • Sander Greenland and James M Robins. Confounding and misclassification. American Journal of Epidemiology, 122(3):495–506, 1985.
  • L. Li, C. Shen, A.C. Wu, and X. Li. Propensity score-based sensitivity analysis method for uncontrolled confounding. American Journal of Epidemiology, 174(3):345–353, 2011.

Propensity score variable selection

  • M.A. Brookhart, S. Schneeweiss, K.J. Rothman, R.J. Glynn, J. Avorn, and T. Stürmer. Variable selection for propensity score models. American journal of epidemiology, 163(12):1149–1156, 2006.
  • J.A. Myers, J.A. Rassen, J.J. Gagne, K.F. Huybrechts, S. Schneeweiss, K.J. Rothman, M.M. Joffe, and R.J. Glynn. Effects of adjusting for instrumental variables on bias and precision of effect estimates. American journal of epidemiology, 174(11):1213–1222, 2011.
  • D. Westreich, S.R. Cole, M.J. Funk, M.A Brookhart, and T. Stürmer. The role of the c-statistic in variable selection for propensity score models. Pharmacoepidemiology and drug safety, 20(3):317–320, 2011.
  • J.A. Myers, J.A. Rassen, J.J. Gagne, K.F. Huybrechts, S. Schneeweiss, K.J. Rothman, and R.J. Glynn. Myers et al. respond to “understanding bias amplification”. American journal of epidemiology, 174(11):1228–1229, 2011.
  • J. Pearl. Invited commentary: understanding bias amplification. American journal of epidemiology, 174(11):1223–1227, 2011.
  • W. Liu, M.A. Brookhart, S. Schneeweiss, X. Mi, and S. Setoguchi. Implications of m bias in epidemiologic studies: a simulation study. American journal of epidemiology, 176(10):938–948, 2012.
  • R. Wyss and T. Stürmer. Balancing automated procedures for confounding control with background knowledge. Epidemiology (Cambridge, Mass.), 25(2):279, 2014.
  • Krista F Huybrechts, M Alan Brookhart, Kenneth J Rothman, Rebecca A Silliman, Tobias Gerhard, Stephen Crystal, and Sebastian Schneeweiss. Comparison of different approaches to confounding adjustment in a study on the association of antipsychotic medication with mortality in older nursing home patients. American journal of epidemiology, 174(9):1089–1099, 2011.
  • John M Brooks and Robert L Ohsfeldt. Squeezing the balloon: propensity scores and unmeasured covariate balance. Health services research, 48(4):1487–1507, 2013.
  • M Alan Brookhart, Til Stürmer, Robert J Glynn, Jeremy Rassen, and Sebastian Schneeweiss. Confounding control in healthcare database research: challenges and potential approaches. Medical care, 48(6 0):S114, 2010.
  • Donald B Rubin. On principles for modeling propensity scores in medical research. Pharmacoepidemiology and drug safety, 13(12):855–857, 2004.
  • Donald B Rubin. The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Statistics in medicine, 26(1):20–36, 2007.
  • E.A. Stuart, B.K. Lee, and F.P. Leacy. Prognostic score–based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research. Journal of clinical epidemiology, 66(8):S84–S90, 2013.
  • J Michael Oakes and Timothy R Church. Invited commentary: advancing propensity score methods in epidemiology. American journal of epidemiology, 165(10):1119–1121, 2007.
  • Layla Parast, Daniel F McCaffrey, Lane F Burgette, Fernando Hoces de la Guardia, Daniela Golinelli, Jeremy NV Miles, and Beth Ann Griffin. Optimizing variance-bias trade-off in the twang package for estimation of propensity scores. Health Services and Outcomes Research Methodology, pages 1–23, 2016.

Propensity score

  • P.R. Rosenbaum and D.B. Rubin. The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1):41–55, 1983.
  • D.B. Rubin. Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127(8 Part 2):757–763, 1997.
  • P.C. Austin. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3):399–424, 2011.
  • Til Stürmer, Sebastian Schneeweiss, Jerry Avorn, and Robert J Glynn. Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. American journal of epidemiology, 162(3):279–289, 2005.
  • P.C. Austin. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine, 28(25):3083–3107, 2009.
  • Til Stürmer, Manisha Joshi, Robert J Glynn, Jerry Avorn, Kenneth J Rothman, and Sebastian Schneeweiss. A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. Journal of clinical epidemiology, 59(5):437–e1, 2006.
  • Wolfgang C Winkelmayer and Tobias Kurth. Propensity scores: help or hype? Nephrology Dialysis Transplantation, 19(7):1671–1673, 2004.
  • Amanda R Patrick, Sebastian Schneeweiss, M Alan Brookhart, Robert J Glynn, Kenneth J Rothman, Jerry Avorn, and Til Stürmer. The implications of propensity score variable selection strategies in pharmacoepidemiology: an empirical illustration. Pharmacoepidemiology and drug safety, 20(6):551–559, 2011.
  • Jessica A Myers and Thomas A Louis. Comparing treatments via the propensity score: stratification or modeling? Health Services and Outcomes Research Methodology, 12(1):29–43, 2012.
  • M Alan Brookhart, Richard Wyss, J Bradley Layton, and Til Stürmer. Propensity score methods for confounding control in nonexperimental research. Circulation: Cardiovascular Quality and Outcomes, 6(5):604–611, 2013.
  • Melissa M Garrido. Covariate adjustment and propensity score. Jama, 315(14):1521–1522, 2016.
  • Steven N Goodman, Sebastian Schneeweiss, and Michael Baiocchi. Using design thinking to differentiate useful from misleading evidence in observational research. Jama, 317(7):705–707, 2017.
  • Baiju R Shah, Andreas Laupacis, Janet E Hux, and Peter C Austin. Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. Journal of clinical epidemiology, 58(6):550–559, 2005.