The Journal of Thoracic and Cardiovascular Surgery
Volume 134, Issue 5 , Pages 1128-1135.e3 , November 2007

Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: A systematic review and suggestions for improvement

  • Peter C. Austin, PhD

      Affiliations

    • Corresponding Author InformationAddress for reprints: Peter C. Austin, PhD, Institute for Clinical Evaluative Sciences, G1 06, 2075 Bayview Ave, Toronto, Ontario M4N 3M5, Canada.

Received 16 April 2007 ,Accepted 31 July 2007.

References 

  1. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55
  2. Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc. 1984;79:516–524
  3. Grunkmeier GL, Payne N, Jin R, Handy JR. Propensity score analysis of stroke after off-pump coronary artery bypass grafting. Ann Thorac Surg. 2002;74:301–305
  4. Blackstone EH. Comparing apples and oranges. J Thorac Cardiovasc Surg. 2002;123:8–15
  5. Austin PC, Mamdani MM, Stukel TA, Anderson GM, Tu JV. The use of the propensity score for estimating treatment effects: administrative versus clinical data. Stat Med. 2005;24:1563–1578
  6. Austin PC, Grootendorst P, Anderson GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: A Monte Carlo study. Stat Med. 2007;26:734–753
  7. Austin PC, Mamdani MM. A comparison of propensity score methods: a case-study estimating the effectiveness of post-AMI statin use. Stat Med. 2006;25:2084–2106
  8. Strasak AM, Zaman Q, Marinell G, Pfeiffer KP, Ulmer H. The use of statistics in medical research: a comparison of The New England Journal of Medicine and Nature Medicine. Am Stat. 2007;61:47–55
  9. Austin PC. A critical appraisal of propensity-score matching in the medical literature from 1996 to 2003. Stat Med. Accepted.
  10. Ho DE, Imai K, King G, Stuart EA. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Anal. In press.
  11. Parson LS. Proceedings of the Twenty-sixth Annual SAS Users Group International Conference. In: Reducing bias in a propensity score matched-pair sample using greedy matching techniques. Cary (NC): SAS Institute Inc; 2001;p. 214–216
  12. Rosenbaum PR. Observational studies. Springer-Verlag: New York; 1995;
  13. Altman DT, Dore CJ. Randomisation and baseline comparisons in clinical trials. Lancet. 1990;335:149–153
  14. Altman DG. Comparability of randomised groups. Statistician. 1985;34:125–136
  15. Senn S. Testing for baseline balance in clinical trials. Stat Med. 1994;13:1715–1726
  16. Senn S. Baseline comparisons in randomized clinical trials. Stat Med. 1991;10:1157–1160
  17. Senn SJ. Covariate imbalance and random allocation in clinical trials. Stat Med. 1989;8:467–475
  18. Begg CB. Significance tests of covariate imbalance in clinical trials. Control Clin Trials. 1990;11:223–225
  19. Rothman KJ. Epidemiologic methods in clinical trials. Cancer. 1977;39:1771–1775
  20. Imai K, King G, Stuart EA. Misunderstandings among experimentalists and observationalists: balance test fallacies in causal inference. Technical report. Princeton University. Available from: http://imai.princeton.edu/research/balance.html.
  21. Flury BK, Riedwyl H. Standard distance in univariate and multivariate analysis. Am Stat. 1986;40:249–251
  22. Normand ST, Landrum MB, Guadagnoli E, Ayanian JZ, Ryan TJ, Cleary PD, et al. Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores. J Clin Epidemiol. 2001;54:387–398
  23. Rothman KJ, Greenland S. Modern epidemiology. Philadelphia: Lippincott Williams & Wilkins; 1998;
  24. Breslow NE, Day NE. Statistical methods in cancer research. The analysis of case–control studies. Vol I. Lyon: International Agency for Research on Cancer; 1980;
  25. Snedecor GW, Cochran WG. Statistical methods. 8th ed.. Ames (IA): Iowa State University Press; 1989;
  26. Conover WJ. Practical nonparametric statistics. 3rd ed.. New York: John Wiley; 1999;
  27. Fleiss JL, Levin B, Paik MC. Statistical methods for rates and proportions. 3rd ed.. New York: John Wiley; 2003;
  28. Agresti A, Min Y. Effects and non-effects of paired identical observations in comparing proportions with binary matched-pairs data. Stat Med. 2004;23:65–75
  29. Klein JP, Moeschberger ML. Survival analysis: techniques for censored and truncated data. New York: Springer-Verlag; 1997;
  30. Harrington D. Encyclopedia of biostatisics. In:  Armitage P,  Colton T editor. Linear rank tests in survival analysis. 2nd ed.. New York: John Wiley; 2005;p. 2802–2812
  31. Cox DR, Oakes K. Analysis of survival data. London: Chapman & Hall; 1984;
  32. Therneau TM, Grambsch PM. Modeling survival data: extending the Cox model. New York: Springer-Verlag; 2000;
  33. Lin DY, Wei LJ. The robust inference for the proportional hazards model. J Am Stat Assoc. 1989;84:1074–1078
  34. Cox DR, Snell EJ. Analysis of binary data. London: Chapman & Hall; 1989;
  35. Diggle PJ, Liang KY, Zeger SL. Analysis of longitudinal data. Oxford: Oxford University Press; 1994;
  36. 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. J Clin Epidemiol. 2006;59:437–447
  37. Rosenbaum PR, Rubin DB. The bias due to incomplete matching. Biometrics. 1985;41:103–116
  38. D’Agostino RB. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med. 1998;17:2265–2281
  39. Joffe MM, Rosenbaum PR. Invited commentary: Propensity scores. Am J Med. 1999;150:327–333
  40. Rubin DB. Estimating causal treatment effects from large datasets using propensity scores. Ann Intern Med. 1997;127:757–763
  41. Cochran WG, Rubin DB. Controlling bias in observational studies: a review. Sankhya Series A. 1973;35:417–446
  42. Moher D, Schulz KF, Altman D CONSORT Group. The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomized trials. JAMA. 2001;285:1987–1991
  43. Austin PC. The performance of different propensity score methods for estimating marginal odds ratios. Stat Med. 2007;26:3078–3094
  44. 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. Stat Med. 2007;26:754–768
  45. Greenland S. Interpretation and choice of effect measures in epidemiologic analyses. Am J Epidemiol. 1987;125:761–768

 The Institute for Clinical Evaluative Sciences (ICES) is supported in part by a grant from the Ontario Ministry of Health and Long Term Care. The opinions, results and conclusions are those of the author and no endorsement by the Ministry of Health and Long-Term Care or by the Institute for Clinical Evaluative Sciences is intended or should be inferred. Dr Austin is supported in part by a New Investigator award from the Canadian Institutes of Health Research (CIHR).

PII: S0022-5223(07)01243-3

doi: 10.1016/j.jtcvs.2007.07.021

The Journal of Thoracic and Cardiovascular Surgery
Volume 134, Issue 5 , Pages 1128-1135.e3 , November 2007

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