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PMID: 26250611 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Optimal full matching for survival outcomes: a method that merits more widespread use.

Statistics in medicine ·Vol. 34 ·No. 30 ·2015-12-30 ·Pages 3949-67

Austin PC, Stuart EA

Abstract

Matching on the propensity score is a commonly used analytic method for estimating the effects of treatments on outcomes. Commonly used propensity score matching methods include nearest neighbor matching and nearest neighbor caliper matching. Rosenbaum (1991) proposed an optimal full matching approach, in which matched strata are formed consisting of either one treated subject and at least one control subject or one control subject and at least one treated subject. Full matching has been used rarely in the applied literature. Furthermore, its performance for use with survival outcomes has not been rigorously evaluated. We propose a method to use full matching to estimate the effect of treatment on the hazard of the occurrence of the outcome. An extensive set of Monte Carlo simulations were conducted to examine the performance of optimal full matching with survival analysis. Its performance was compared with that of nearest neighbor matching, nearest neighbor caliper matching, and inverse probability of treatment weighting using the propensity score. Full matching has superior performance compared with that of the two other matching algorithms and had comparable performance with that of inverse probability of treatment weighting using the propensity score. We illustrate the application of full matching with survival outcomes to estimate the effect of statin prescribing at hospital discharge on the hazard of post-discharge mortality in a large cohort of patients who were discharged from hospital with a diagnosis of acute myocardial infarction. Optimal full matching merits more widespread adoption in medical and epidemiological research. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Keywords
Monte Carlo simulations bias full matching matching observational studies optimal matching propensity score
MeSH Terms
Biostatistics/methods Case-Control Studies Computer Simulation Confidence Intervals Female Humans Male Models, Statistical Monte Carlo Method Myocardial Infarction/mortality,therapy Outcome Assessment, Health Care/statistics & numerical data Propensity Score Proportional Hazards Models Regression Analysis Survival Analysis
Authors & Affiliations
2 authors, click to expand affiliations / ORCID
Austin Peter C
Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. | Institute of Health Management, Policy and Evaluation, University of Toronto, Toronto, Ontario, Canada. | Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada.
Stuart Elizabeth A
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A. | Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A. | Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A.
References (38)
38 references, click to expand
  1. Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases.
    Comput Stat Data Anal. 2014 Apr;72:219-226 PMID: 24587587
  2. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments.
    Stat Med. 2014 Mar 30;33(7):1242-58 PMID: 24122911
  3. Interval estimation for treatment effects using propensity score matching.
    Stat Med. 2006 Jul 15;25(13):2230-56 PMID: 16220488
  4. 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 Feb 20;26(4):734-53 PMID: 16708349
  5. The use of bootstrapping when using propensity-score matching without replacement: a simulation study.
    Stat Med. 2014 Oct 30;33(24):4306-19 PMID: 25087884
  6. On principles for modeling propensity scores in medical research.
    Pharmacoepidemiol Drug Saf. 2004 Dec;13(12):855-7 PMID: 15386710
  7. Matching methods for causal inference: A review and a look forward.
    Stat Sci. 2010 Feb 1;25(1):1-21 PMID: 20871802
  8. Substantial gains in bias reduction from matching with a variable number of controls.
    Biometrics. 2000 Mar;56(1):118-24 PMID: 10783785
  9. Two propensity score-based strategies for a three-decade observational study: investigating psychotropic medications and suicide risk.
    Stat Med. 2012 Nov 30;31(27):3255-60 PMID: 22865578
  10. The performance of different propensity score methods for estimating marginal hazard ratios.
    Stat Med. 2013 Jul 20;32(16):2837-49 PMID: 23239115
  11. The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies.
    Stat Med. 2010 Sep 10;29(20):2137-48 PMID: 20108233
  12. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies.
    Pharm Stat. 2011 Mar-Apr;10(2):150-61 PMID: 20925139
  13. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study.
    Stat Med. 2007 Feb 20;26(4):754-68 PMID: 16783757
  14. Factors associated with racial differences in myocardial infarction outcomes.
    Ann Intern Med. 2009 Mar 3;150(5):314-24 PMID: 19258559
  15. 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 May;59(5):437-47 PMID: 16632131
  16. The performance of inverse probability of treatment weighting and full matching on the propensity score in the presence of model misspecification when estimating the effect of treatment on survival outcomes.
    Stat Methods Med Res. 2017 Aug;26(4):1654-1670 PMID: 25934643
  17. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples.
    Stat Med. 2009 Nov 10;28(25):3083-107 PMID: 19757444
  18. A comparison of 12 algorithms for matching on the propensity score.
    Stat Med. 2014 Mar 15;33(6):1057-69 PMID: 24123228
  19. Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data.
    Stat Sci. 2007;22(4):569-573 PMID: 18516239
  20. Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study.
    Pharm Stat. 2012 May-Jun;11(3):222-9 PMID: 22411785
  21. Antiepileptic drugs for bipolar disorder and the risk of suicidal behavior: a 30-year observational study.
    Am J Psychiatry. 2012 Mar;169(3):285-91 PMID: 22193537
  22. The performance of different propensity score methods for estimating marginal odds ratios.
    Stat Med. 2007 Jul 20;26(16):3078-94 PMID: 17187347
  23. Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: a systematic review and suggestions for improvement.
    J Thorac Cardiovasc Surg. 2007 Nov;134(5):1128-35 PMID: 17976439
  24. Reader's guide to critical appraisal of cohort studies: 2. Assessing potential for confounding.
    BMJ. 2005 Apr 23;330(7497):960-2 PMID: 15845982
  25. A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003.
    Stat Med. 2008 May 30;27(12):2037-49 PMID: 18038446
  26. The design of simulation studies in medical statistics.
    Stat Med. 2006 Dec 30;25(24):4279-92 PMID: 16947139
  27. Primer on statistical interpretation or methods report card on propensity-score matching in the cardiology literature from 2004 to 2006: a systematic review.
    Circ Cardiovasc Qual Outcomes. 2008 Sep;1(1):62-7 PMID: 20031790
  28. Generating survival times to simulate Cox proportional hazards models.
    Stat Med. 2005 Jun 15;24(11):1713-23 PMID: 15724232
  29. Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model.
    JAMA. 2003 Nov 19;290(19):2581-7 PMID: 14625335
  30. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies.
    Multivariate Behav Res. 2011 May;46(3):399-424 PMID: 21818162
  31. A Tutorial and Case Study in Propensity Score Analysis: An Application to Estimating the Effect of In-Hospital Smoking Cessation Counseling on Mortality.
    Multivariate Behav Res. 2011;46(1):119-151 PMID: 22287812
  32. Mortality following nursing home-acquired lower respiratory infection: LRI severity, antibiotic treatment, and water intake.
    J Am Med Dir Assoc. 2012 May;13(4):376-83 PMID: 21514897
  33. The performance of different propensity-score methods for estimating relative risks.
    J Clin Epidemiol. 2008 Jun;61(6):537-45 PMID: 18471657
  34. Statistical criteria for selecting the optimal number of untreated subjects matched to each treated subject when using many-to-one matching on the propensity score.
    Am J Epidemiol. 2010 Nov 1;172(9):1092-7 PMID: 20802241
  35. Effectiveness of public report cards for improving the quality of cardiac care: the EFFECT study: a randomized trial.
    JAMA. 2009 Dec 2;302(21):2330-7 PMID: 19923205
  36. Average causal effects from nonrandomized studies: a practical guide and simulated example.
    Psychol Methods. 2008 Dec;13(4):279-313 PMID: 19071996
  37. Optimal full matching for survival outcomes: a method that merits more widespread use.
    Stat Med. 2015 Dec 30;34(30):3949-67 PMID: 26250611
  38. A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals.
    J Clin Epidemiol. 2010 Feb;63(2):142-53 PMID: 19716262
Article Info
Journal
Statistics in medicine
Abbr.
Stat Med
ISSN
1097-0258
DOI
10.1002/sim.6602
Published
2015-12-30
Epub
2015-00-06
Pages
3949-67
Language
English
Region
England
NLM ID
8215016
PMCID
PMC4715723
Subset
IM
Grants
NIMH NIH HHS · R01 MH099010 · United States
Canadian Institutes of Health Research · MOP 86508 · Canada
NIMH NIH HHS · R01MH099010 · United States
External Links
PubMed source DOI: 10.1002/sim.6602 PMC Full Text View journal details
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