• 1
    Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70:4155.
  • 2
    Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 1984; 79:516524.
  • 3
    Austin PC, Mamdani MM. A comparison of propensity score methods: a case-study estimating the effectiveness of post-AMI statin use. Statistics in Medicine 2006; 25:20842106.
  • 4
    Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician 1985; 39:3338.
  • 5
    Austin PC. A critical appraisal of propensity score matching in the medical literature from 1996 to 2003. Statistics in Medicine 2008; 27:20372049.
  • 6
    Austin PC. Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: a systematic review and suggestions for improvement. Journal of Thoracic and Cardiovascular Surgery 2007; 134:11281135.
  • 7
    Austin PC. A report card on propensity-score matching in the cardiology literature from 2004 to 2006: results of a systematic review. Circulation: Cardiovascular Quality and Outcomes 2008; 1:6267.
  • 8
    Imbens GW. Nonparametric estimation of average treatment effects under exogeneity: a review. The Review of Economics and Statistics 2004; 86:429.
  • 9
    Austin PC. Type I error rates, coverage of confidence intervals, and variance estimation in propensity-score matched analyses. The International Journal of Biostatistics 2009; 5(1). Article 13. DOI: 10.2202/1557-4679.1146.
  • 10
    Stürmer T, Joshi M, Glynn RJ, Avorn J, Rothman KJ, Schneeweiss S. 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 2006; 59:437447.
  • 11
    Schechtman E. Odds ratio, relative risk, absolute risk reduction, and the number needed to treat–which of these should we use? Value in Health 2002; 5:431436.
    Direct Link:
  • 12
    Cook RJ, Sackett DL. The number needed to treat: a clinically useful measure of treatment effect. British Medical Journal 1995; 310:452454.
  • 13
    Jaeschke R, Guyatt G, Shannon H, Walter S, Cook D, Heddle N. Basis statistics for clinicians 3: assessing the effects of treatment: measures of association. Canadian Medical Association Journal 1995; 152:351357.
  • 14
    Sinclair JC, Bracken MB. Clinically useful measures of effect in binary analyses of randomized trials. Journal of Clinical Epidemiology 1994; 47:881889.
  • 15
    Austin PC, Grootendorst P, Normand SLT, Anderson GM. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study. Statistics in Medicine 2007; 26:754768.
  • 16
    Austin PC. The performance of different propensity score methods for estimating marginal odds ratios. Statistics in Medicine. 2007; 26:30783094.
  • 17
    Greenland S. Interpretation and choice of effect measures in epidemiologic analyses. American Journal of Epidemiology 1987; 125:761768.
  • 18
    Austin PC. A data-generation process for data with specified risk differences or numbers needed to treat. Communications in Statistics–Simulation and Computation 2010; 39:563577.
  • 19
    Austin PC. The performance of different propensity score methods for estimating difference in proportions (risk differences or absolute risk reductions) in observational studies. Statistics in Medicine 2010; DOI: 10.1002/sim.3854.
  • 20
    Austin PC, Stafford J. The performance of two data-generation processes for data with specified marginal treatment odds ratios. Communications in Statistics–Simulation and Computation 2008; 37:10391051.
  • 21
    Cohen J. Statistical Power Analysis for the Behavioral Sciences (2nd edn). Lawrence Erlbaum Associates Publishers: Hillsdale NJ, 1988.
  • 22
    Cochran WG, Rubin DB. Controlling bias in observational studies: a review. Sankhya: The Indian Journal of Statistics 1973;35:416466.
  • 23
    Agresti A, Min Y. Effects and non-effects of paired identical observations in comparing proportions with binary matched-pairs data. Statistics in Medicine 2004; 23:6575.
  • 24
    Lee DS, Austin PC, Rouleau JL, Liu PP, Naimark D, Tu JV. Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model. Journal of the American Medical Association 2003; 290:25812587.
  • 25
    Tu JV, Donovan LR, Lee DS, Austin PC, Ko DT, Wang JT, Newman AM. Quality of Cardiac Care in Ontario–Phase 1. Report 1. Institute for Clinical Evaluative Sciences: Toronto, 2004.
  • 26
    Flury BK, Riedwyl H. Standard distance in univariate and multivariate analysis. The American Statistician 1986; 40:249251.
  • 27
    Austin PC. Some methods of propensity-score matching had superior performance to others: results of an empirical investigation and Monte Carlo simulations. Biometrical Journal 2009; 51:171184. DOI: 10.1002/bimj.200810488.
  • 28
    Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine 2009; 28:30833107.
  • 29
    Austin PC. The relative ability of different propensity-score methods to balance measured covariates between treated and untreated subjects in observational studies. Medical Decision Making 2009; 29:661677.