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METHODOLOGICAL STUDIES

Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies

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References

  • An, W.H. (2010). Bayesian propensity score estimators: Incorporating uncertainties in propensity scores into causal inference. Sociological Methodology, 40, 151–189.
  • Austin, P.C. (2008). A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Statistics in Medicine, 27, 2037–2049.
  • Austin, P.C., Mamdani, M.M. (1993). Effects of misspecification of the propensity score on estimators of treatment effect. Biometrics, 49, 1231–1236.
  • Austin, P.C., Mamdani, M.M. (2006). A comparison of propensity score methods: A case-study estimating the effectiveness of post-AMI statin use. Statistics in Medicine, 25, 2084–2106.
  • Ayanian, J.Z., Landrum, M.B., Guadagnoli, E.Gaccione, P. (2002). Specialty of ambulatory care physicians and mortality among elderly patients after myocardial infarction. New England Journal of Medicine, 347, 1678–1686.
  • Cochran, W.G. (1968). The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies. Biometrics, 24, 205–213.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences, 2nd edition. Hillsdale NJ: Erlbaum.
  • De Finetti, B. (1974). Theory of Probability, Vols. 1 and 2.New YorkNY: Wiley and Sons.
  • Dehejia, R.H., Wahba, S. (1999). Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs. Journal of the American Statistical Association, 94, 1053–1062.
  • Dehejia, R.H., Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. The Review of Economics and Statistics, 84, 151–161.
  • Foster, E.M. (2003). Propensity Score Matching An Illustrative Analysis of Dose Response. Medical Care, 41, 1183–1192.
  • Gelman, A.Jakulin, A.Pittau, M.G., Su, Y.S. (2008). A weakly informative default prior distribution for logistic and other regression models. The Annals of Applied Statistics, 2, 1360–1383.
  • Gelman, A.Su, Y.S., Yajima, M.Hill, J.L., Pittau, M.G., Kerman, J.Zheng, T. (2011). arm: Data Analysis Using Regression and Multilevel/Hierarchical Modelsm, arm: Data analysis using regression and multilevel/hierarchical models (R package version 1.4–13) [Computer software mannual]. Availabel from http://CRAN.R-project.org/package=arm
  • Gilks, W.R., Richardson, S.,& Spiegelhalter, D.J. (Ed.) (1996). Markov chain Monte Carlo in practice. London: Chapman & Hall/CRC.
  • Guo, S.Barth, R.P., Gibbons, C. (2006). Propensity score matching strategies for evaluating substance abuse services for child welfare clients. Children and Youth Services Review, 28, 357–383.
  • Hansen, B.B. (2004). Full matching in an observational study of coaching for the sat. Journal of the American Statistical Association, 99, 609–618.
  • Hansen, B.B. (2008). The essential role of balance tests in propenisty-matched observational studies: Comments on “A Critical Appraisal of Propensity-Score Matching in the Medical Literature Between 1996 and 2003” by Peter Austin. Statistics in Medicine, 27, 2050–2054.
  • Hansen, B.B., Klopfer, S.O. (2006). Optimal full matching and related designs via network flow. Journal of Computational and Graphical Statistics, 15, 609–627.
  • Harder, V.S., Stuart, E.A., Anthony, J.C. (2010). Propensity Score Techniques and the Assessment of Measured Covariate Balance to Test Causal Associations in Psychological Research. Psychological Methods, 15, 234–249.
  • Hill, J.L. (2008). Discussion of research using propensity-score matching: Comments on “A Critical Appraisal of Propensity-Score Matching in the Medical Literature Between 1996 and 2003" by Peter Austin. Statistics in Medicine, 27, 2055–2061.
  • Hill, J.L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20, 217–240.
  • Hirano, K.Imbens, G.W. (2001). Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization. Health Services and Outcomes Research Methodology, 2, 259–278.
  • Hirano, K., Imbens, G.W., Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71, 1169–1189.
  • Horvitz, D.G., Thompson, D.J. (1952). A generalization of sampling without replacement fiom a finite universe. Journal of the American Statistical Association, 47, 663–685.
  • Hoshino, T. (2008). A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm. Computational Statistics & Data Analysis, 52, 1413–1429.
  • Imai, K., King, G.Stuart, E.A. (2008). Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society, Series A, 171, 481–502.
  • Kang, J.Schafer, J.L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science, 22, 523–539.
  • Kaplan, D. (2014). Bayesian statistics for the social sciences. New York: The Guilford Press.
  • Kaplan, D.Chen, J. (2012). A two-step Bayesian approach for propensity score analysis: Simulations and case study. Psychometrika, 77, 581–609.
  • Kurth, T.Walker, A.M., Glynn, R.J., Chan, K.A., Gaziano, M.J., Berger, K.Robins, J.M. (2006). Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. American Journal of Epidemiology, 163, 262–270.
  • Leow, C.Marcus, S.Zanutto, E.Boruch, R. (2004). Effects of advanced course-taking on math and science achievement: Addressing selection bias using propensity scores. American Journal of Evaluation, 25, 461–478.
  • Lunceford, J.Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative studyStatistics in Medicine, 23, 2937–2960.
  • Lunn, D.Best, N.Spiegelhalter, D.Graham, G.Neuenschwander, B. (2009). Combining mcmc with “sequential” PKPD modelling. Journal of Pharmacokinetics and Pharmacodynamics, 36, 19–38.
  • Martin, A.D., Quinn, K.M., Park, J.H. (2010). Markov chain Monte Carlo (MCMC) package. Retrived form http://mcmcpack.wustl.edu/
  • McCaffrey, D.F., Ridgeway, G.Morral, A.R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9, 403–425.
  • McCandless, L.C., Douglas, I.J., Evans, S.J., Smeeth, L. (2010). Cutting feedback in Bayesian regression adjustment for the propensity score.The International Journal of Biostatistics, 6, Article 16.
  • McCandless, L.C., Gustafson, P.Austin, P.C. (2009). Bayesian propensity score analysis for observational data. Statistics in Medicine, 28, 94–112.
  • McCandless, L.C., Gustafson, P.Austin, P.C., Levy, A.R. (2009). Covariate balance in a Bayesian propensity score analysis of beta blocker therapy in heart failure parients. Epidemiologic Perspectives & Innovations, 6, 5–15.
  • NCES., (2001). Early childhood longitudinal study: Kindergarten class of 1998-99: Base year public-use data files user’s manual (Tech. Rep. No. NCES 2001-029). Washington DC: U.S. Department of Education
  • (2011). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. Available from http://www.R-project.org
  • Rosenbaum, P.R. (1987). Model-based direct adjustment. Journal of the American Statistical Association, 82, 387–394.
  • Rosenbaum, P.R. (1989). Optimal matching for observational studies. Journal of the American Statistical Association, 84, 1024–1032.
  • Rosenbaum, P.R., Rubin, D.B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.
  • Rosenbaum, P.R., Rubin, D.B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79, 516–524.
  • Rubin, D.B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.
  • Rubin, D.B. (1979). Using multivariate matched sampling and regression adjustment to control bias in observational studies. Journal of the American Statistical Association, 74, 318–328.
  • Rubin, D.B. (1985). The use of propensity scores in applied Bayesian inference. Bayesian Statistics, 2, 463–472.
  • Rubin, D.B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services and Outcomes Research Methodology, 2, 169188.
  • Schafer, J.L., Kang, J. (2008). Average causal effects from non-randomized studies: A practical guide and simulated example. Psychological Methods, 13, 279–313.
  • Shadish, W.R., Clark, M.H., Steiner, P.M. (2008). Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random and nonrandom assignments. Journal of the American Statistical Association, 103, 1334–1356.
  • Steiner, P.M., & Cook, D. L. (2013). Matching and propensity scores. In T. D. Little (Ed.), The Oxford handbook of quantitative mqthods. New York: Oxford University Press.
  • Stuart, E.A. (2008). Developing practical recommendations for the use of propensity scores: Discussion of “A Critical Appraisal of Propensity-Score Matching in the Medical Literature Between 1996 and 2003.” by Peter Austin.Statistics in Medicine, 27, 2062–2065.
  • Swanson, J.M., Hinshaw, S.P., Arnold, E.Gibbons, R.D., Marcus, S.Hur, K., … Wigal, T. (2007). Secondary evaluations of MTA 36-month outcomes: Propensity score and growth mixture model analyses. Journal of the American Academy of Child & Adolescent Psychiatry, 46, 1003–1014.
  • Van Buuren, S.Groothuis-Oudshoorn, K. (2011). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45, 1–67.
  • Wooldridge, J.M. (2002). Inverse probability weighted m-estimators for sample selection, attrition, and stratification. Portuguese Economic Journal, 1, 117–139.
  • Zigler, C.M., Watts, K.Yeh, R.W., Wang, Y.Coull, B.A., Dominici, F. (2013). Model feedback in bayesian propensity score estimation. Biometrics, 69, 263–273.

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