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  • Braitman , L. E. & P. R. Rosenbaum : Rare outcomes, common treatments: analytic strategies using propensity scores. Ann. Intern. Med. 2002, 137, 693695.
  • Brookhart , M. A. , S. Schneeweiss , K. J. Rothman , R. J. Glynn , J. Avorn & T. Stürmer : Variable selection in propensity score models. Amer. J. Epidemiol. 2006, in press.
  • Carroll , R. J. , D. Ruppert & L. A. Stefanski : Measurement error in nonlinear models. Chapman and Hall, London, 1995.
  • Cepeda , M. S. , R. Boston , J. T. Farrar & B. L. Strom : Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. Amer. J. Epidemiol. 2003, 158, 280287.
  • Cook , E. F. & L. Goldman : Performance of tests of significance based on stratification by a multivariate confounder score or by a propensity score. J. Clin. Epidemiol. 1989, 42, 317324.
  • Cook , E. F. & L. Goldman : Asymmetric stratification. An outline for an efficient method for controlling confounding in cohort studies. Amer. J. Epidemiol. 1988, 127, 626639.
  • D'Agostino , R. B. Jr. : Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat. Med. 1998, 17, 22652281.
  • Drake , C. : Effects of misspecification of the propensity score on estimators of treatment effect. Biometrics 1993, 49, 12311236.
  • Glynn , R. J. , E. L. Knight , R. Levin & J. Avorn : Paradoxical relations of drug treatment with mortality in older persons. Epidemiology 2001, 12, 682689.
  • Glynn , R. J. , S. Schneeweiss , P. S. Wang , R. Levin & J. Avorn : Selective prescribing led to over-estimation of the benefits of lipid-lowering drugs. J. Clin. Epidemiol. 2006, in press.
  • Gu , X. S. & P. R. Rosenbaum : Comparison of multivariate matching methods: structures, distances and algorithms. J. Computat. Graph. Stat. 1993, 2, 405420.
  • Harrell , F. E. Jr. , K. L. Lee & D. B. Mark : Multivariable prognostic models. Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 1996, 15, 361387.
  • Joffe , M. M. & P. R. Rosenbaum : Invited commentary: propensity scores. Amer. J. Epidemiol. 1999, 150, 327333.
  • Kurth , T. , A. M. Walker , R. J. Glynn , K. A. Chan , J. M. Gaziano , J. M. Robins & K. Berger : Results of multivariable logistic regression, propensity matching, propensity adjustment and propensity-based weighting under conditions of non-uniform effect. Amer. J. Epidemiol. 2006, in press.
  • Miettinen , O. S. : Stratification by a multivariate confounder score. Amer. J. Epidemiol. 1976, 104, 609620.
  • Peduzzi , P. , J. Concato , E. Kemper , T. R. Holford & A. R. Feinstein : A simulation study of the number of events per variab le in logistic regression. J. Clin. Epidemiol. 1996, 49, 13731379.
  • Pike , M. C. , J. Anderson & N. Day : Some insights into Miettinen's multivariate confounder score approach to case-control study analysis. Epidemiol. Comm. Health 1979, 33, 104106.
  • Ray , W. A. , C. M. Stein , K. Hall , J. R. Daugherty & M. R. Griffin : Non-steroidal anti-inflammatory drugs and risk of serious coronary heart disease: an observational cohort study. Lancet 2002, 359, 118123.
  • Robins , J. M. , M. A. Hernan & B. Brumback : Marginal structural models and causal inference in epidemiology. Epidemiology 2000, 11, 550560.
  • Rosenbaum , P. R. & D. B. Rubin : The central role of the propensity score in observational studies for causal effects. Biometrika 1983, 70, 4155.
  • Rosenbaum , P. R. & D. B. Rubin : Reducing bias in observational studies using subclassification on the propensity score. J. Amer. Statist. Assoc. 1984, 79, 516524.
  • Rosenbaum , P. R. & D. B. Rubin : Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Amer. Statist. 1985, 39, 3338.
  • Rosenbaum , P. R. : Propensity score. In : Encyclopedia of biostatistics, Second edition. Eds.: P.Armitage & T.Colton . John Wiley and Sons, Chichester, 2005, pp. 42674272.
  • Rubin , D. B. : Estimating causal effects from large data sets using the propensity score. Ann. Intern. Med. 1997, 127, 757763.
  • Rubin , D. B. : On principles for modeling propensity scores in medical research. Pharmacoepidemiol. Drug Safety 2004, 13, 855857.
  • Shah , B. R. , A. Laupacis , J. E. Hux & P. C. Austin : Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J. Clin. Epidemiol. 2005, 58, 550559.
  • Stürmer , T. , M. Joshi , R. J. Glynn , J. Avorn , K. Rothman & S. Schneeweiss : A review of the application of propensity score methods yielded increased use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. J. Clin. Epidemiol. 2006, in press.
  • Stürmer , T. , S. Schneeweiss , J. Avorn & R. J. Glynn : Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. Amer. J. Epidemiol. 2005a, 162, 279289.
  • Stürmer , T. , S. Schneeweiss , M. A. Brookhart , K. J. Rothman , J. Avorn & R. J. Glynn : Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: nonsteroidal anti-inflammatory drugs and short-term mortality in the elderly. Amer. J. Epidemiol. 2005b, 161, 891898.
  • Weitzen , S. , K. L. Lapane , A. Y. Toledano , A. L. Hume & V. Mor : Weaknesses of goodness-of-fit tests for evaluating propensity score models: the case of omitted confounders. Pharmacoepidemiol. Drug Safety 2005, 14, 227238.