The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies
Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensi...
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| Published in | Statistics in medicine Vol. 29; no. 20; pp. 2137 - 2148 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
10.09.2010
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensity score, propensity‐score matching, and inverse probability of treatment weighting (IPTW) using the propensity score. When outcomes are binary, the effect of treatment on the outcome can be described using odds ratios, relative risks, risk differences, or the number needed to treat. Several clinical commentators suggested that risk differences and numbers needed to treat are more meaningful for clinical decision making than are odds ratios or relative risks. However, there is a paucity of information about the relative performance of the different propensity‐score methods for estimating risk differences. We conducted a series of Monte Carlo simulations to examine this issue. We examined bias, variance estimation, coverage of confidence intervals, mean‐squared error (MSE), and type I error rates. A doubly robust version of IPTW had superior performance compared with the other propensity‐score methods. It resulted in unbiased estimation of risk differences, treatment effects with the lowest standard errors, confidence intervals with the correct coverage rates, and correct type I error rates. Stratification, matching on the propensity score, and covariate adjustment using the propensity score resulted in minor to modest bias in estimating risk differences. Estimators based on IPTW had lower MSE compared with other propensity‐score methods. Differences between IPTW and propensity‐score matching may reflect that these two methods estimate the average treatment effect and the average treatment effect for the treated, respectively. Copyright © 2010 John Wiley & Sons, Ltd. |
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| AbstractList | Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensity score, propensity-score matching, and inverse probability of treatment weighting (IPTW) using the propensity score. When outcomes are binary, the effect of treatment on the outcome can be described using odds ratios, relative risks, risk differences, or the number needed to treat. Several clinical commentators suggested that risk differences and numbers needed to treat are more meaningful for clinical decision making than are odds ratios or relative risks. However, there is a paucity of information about the relative performance of the different propensity-score methods for estimating risk differences. We conducted a series of Monte Carlo simulations to examine this issue. We examined bias, variance estimation, coverage of confidence intervals, mean-squared error (MSE), and type I error rates. A doubly robust version of IPTW had superior performance compared with the other propensity-score methods. It resulted in unbiased estimation of risk differences, treatment effects with the lowest standard errors, confidence intervals with the correct coverage rates, and correct type I error rates. Stratification, matching on the propensity score, and covariate adjustment using the propensity score resulted in minor to modest bias in estimating risk differences. Estimators based on IPTW had lower MSE compared with other propensity-score methods. Differences between IPTW and propensity-score matching may reflect that these two methods estimate the average treatment effect and the average treatment effect for the treated, respectively. Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensity score, propensity-score matching, and inverse probability of treatment weighting (IPTW) using the propensity score. When outcomes are binary, the effect of treatment on the outcome can be described using odds ratios, relative risks, risk differences, or the number needed to treat. Several clinical commentators suggested that risk differences and numbers needed to treat are more meaningful for clinical decision making than are odds ratios or relative risks. However, there is a paucity of information about the relative performance of the different propensity-score methods for estimating risk differences. We conducted a series of Monte Carlo simulations to examine this issue. We examined bias, variance estimation, coverage of confidence intervals, mean-squared error (MSE), and type I error rates. A doubly robust version of IPTW had superior performance compared with the other propensity-score methods. It resulted in unbiased estimation of risk differences, treatment effects with the lowest standard errors, confidence intervals with the correct coverage rates, and correct type I error rates. Stratification, matching on the propensity score, and covariate adjustment using the propensity score resulted in minor to modest bias in estimating risk differences. Estimators based on IPTW had lower MSE compared with other propensity-score methods. Differences between IPTW and propensity-score matching may reflect that these two methods estimate the average treatment effect and the average treatment effect for the treated, respectively. Copyright © 2010 John Wiley & Sons, Ltd. Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensity score, propensity-score matching, and inverse probability of treatment weighting (IPTW) using the propensity score. When outcomes are binary, the effect of treatment on the outcome can be described using odds ratios, relative risks, risk differences, or the number needed to treat. Several clinical commentators suggested that risk differences and numbers needed to treat are more meaningful for clinical decision making than are odds ratios or relative risks. However, there is a paucity of information about the relative performance of the different propensity-score methods for estimating risk differences. We conducted a series of Monte Carlo simulations to examine this issue. We examined bias, variance estimation, coverage of confidence intervals, mean-squared error (MSE), and type I error rates. A doubly robust version of IPTW had superior performance compared with the other propensity-score methods. It resulted in unbiased estimation of risk differences, treatment effects with the lowest standard errors, confidence intervals with the correct coverage rates, and correct type I error rates. Stratification, matching on the propensity score, and covariate adjustment using the propensity score resulted in minor to modest bias in estimating risk differences. Estimators based on IPTW had lower MSE compared with other propensity-score methods. Differences between IPTW and propensity-score matching may reflect that these two methods estimate the average treatment effect and the average treatment effect for the treated, respectively. [PUBLICATION ABSTRACT] Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensity score, propensity-score matching, and inverse probability of treatment weighting (IPTW) using the propensity score. When outcomes are binary, the effect of treatment on the outcome can be described using odds ratios, relative risks, risk differences, or the number needed to treat. Several clinical commentators suggested that risk differences and numbers needed to treat are more meaningful for clinical decision making than are odds ratios or relative risks. However, there is a paucity of information about the relative performance of the different propensity-score methods for estimating risk differences. We conducted a series of Monte Carlo simulations to examine this issue. We examined bias, variance estimation, coverage of confidence intervals, mean-squared error (MSE), and type I error rates. A doubly robust version of IPTW had superior performance compared with the other propensity-score methods. It resulted in unbiased estimation of risk differences, treatment effects with the lowest standard errors, confidence intervals with the correct coverage rates, and correct type I error rates. Stratification, matching on the propensity score, and covariate adjustment using the propensity score resulted in minor to modest bias in estimating risk differences. Estimators based on IPTW had lower MSE compared with other propensity-score methods. Differences between IPTW and propensity-score matching may reflect that these two methods estimate the average treatment effect and the average treatment effect for the treated, respectively.Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensity score, propensity-score matching, and inverse probability of treatment weighting (IPTW) using the propensity score. When outcomes are binary, the effect of treatment on the outcome can be described using odds ratios, relative risks, risk differences, or the number needed to treat. Several clinical commentators suggested that risk differences and numbers needed to treat are more meaningful for clinical decision making than are odds ratios or relative risks. However, there is a paucity of information about the relative performance of the different propensity-score methods for estimating risk differences. We conducted a series of Monte Carlo simulations to examine this issue. We examined bias, variance estimation, coverage of confidence intervals, mean-squared error (MSE), and type I error rates. A doubly robust version of IPTW had superior performance compared with the other propensity-score methods. It resulted in unbiased estimation of risk differences, treatment effects with the lowest standard errors, confidence intervals with the correct coverage rates, and correct type I error rates. Stratification, matching on the propensity score, and covariate adjustment using the propensity score resulted in minor to modest bias in estimating risk differences. Estimators based on IPTW had lower MSE compared with other propensity-score methods. Differences between IPTW and propensity-score matching may reflect that these two methods estimate the average treatment effect and the average treatment effect for the treated, respectively. |
| Author | Austin, Peter C. |
| AuthorAffiliation | a Institute for Clinical Evaluative Sciences Toronto, ON, Canada b Department of Health Management, Policy and Evaluation, University of Toronto ON, Canada |
| AuthorAffiliation_xml | – name: a Institute for Clinical Evaluative Sciences Toronto, ON, Canada – name: b Department of Health Management, Policy and Evaluation, University of Toronto ON, Canada |
| Author_xml | – sequence: 1 givenname: Peter C. surname: Austin fullname: Austin, Peter C. email: peter.austin@ices.on.ca organization: Institute for Clinical Evaluative Sciences, Toronto, ON, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/20108233$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Copyright © 2010 John Wiley & Sons, Ltd. Copyright John Wiley and Sons, Limited Sep 10, 2010 |
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| DOI | 10.1002/sim.3854 |
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| References | Austin PC. A data-generation process for data with specified risk differences or numbers needed to treat. Communications in Statistics-Simulation and Computation, in press. Weitzen S, Lapane KL, Toledano AY, Hume AL, Mor V. Principles for modeling propensity scores in medical research: a systematic literature review. Pharmacoepidemiology and Drug Safety 2004; 13:841-853. Sinclair JC, Bracken MB. Clinically useful measures of effect in binary analyses of randomized trials. Journal of Clinical Epidemiology 1994; 47:881-889. Austin PC. The performance of different propensity score methods for estimating relative risks. Journal of Clinical Epidemiology 2008; 61:537-545. 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; 51:171-184. Austin PC. A report card on propensity-score matching in the cardiology literature from 2004-2006: results of a systematic review. Circulation: Cardiovascular Quality and Outcomes 2008; 1:62-67. Greenland S. Interpretation and choice of effect measures in epidemiologic analyses. American Journal of Epidemiology 1987; 125:761-768. Austin PC, Manca A, Zwarenstein M, Juurlink DN, Stanbrook MB. A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals. Journal of Clinical Epidemiology 2010; 63:142-153. DOI: 10.1016/j.jclinepi.2009.06.002. 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; 29:661-677. 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. Statistics in Medicine 2007; 26:734-753. 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; 290:2581-2587. 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; 152:351-357. Rubin DB. On principles for modeling propensity scores in medical research. Pharmacoepidemiology and Drug Safety 2004; 13:885-887. Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine 2004; 23:2937-2960. Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 1984; 79:516-524. Shah BR, Laupacis A, Hux JE, Austin PC. Propensity score methods give similar results to traditional regression modeling in observational studies: a systematic review. Journal of Clinical Epidemiology 2005; 58:550-559. Imbens GW. Nonparametric estimation of average treatment effects under exogeneity: a review. The Review of Economics and Statistics 2004; 86:4-29. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70:41-55. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine 2009; 28:3083-3107. Austin PC. A critical appraisal of propensity score matching in the medical literature from 1996 to 2003. Statistics in Medicine 2008; 27:2037-2049. Agresti A, Min Y. Effects and non-effects of paired identical observations in comparing proportions with binary matched-pairs data. Statistics in Medicine 2004; 23:65-75. Cook RJ, Sackett DL. The number needed to treat: a clinically useful measure of treatment effect. British Medical Journal 1995; 310:452-454. Rosenbaum PR. Model-based direct adjustment. The Journal of the American Statistician 1987; 82:387-394. Austin PC. The performance of different propensity score methods for estimating marginal odds ratios. Statistics in Medicine 2007; 26:3078-3094. 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; 37:1039-1051. 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; 25:2084-2106. 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; 5:431-436. Flury BK, Riedwyl H. Standard distance in univariate and multivariate analysis. The American Statistician 1986; 40:249-251. 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; 26:754-768. 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:Article 13. DOI: 10.2202/1557-4679.1146. McCullagh N, Nelder JA. Generalized Linear Models (2nd edn). Chapman & Hall: London, 1989. Austin PC. Propensity-score matching in the cardiovascular surgery literature from 2004-2006: a systematic review and suggestions for improvement. Journal of Thoracic and Cardiovascular Surgery 2007; 134:1128-1135. 2004; 86 1987; 125 2002; 5 2004; 23 2008 2008; 37 1994; 47 2003; 290 2004 1995; 310 1983; 70 2008; 1 1995; 152 2010; 63 2009; 29 2009; 28 2007; 134 2009; 51 1986; 40 1987; 82 2008; 27 2006; 25 1984; 79 2004; 13 2009; 5 2008; 61 2007; 26 1989 2005; 58 10.1002/sim.3854-BIB5|cit5 Austin (10.1002/sim.3854-BIB25|cit25) 2006; 25 Imbens (10.1002/sim.3854-BIB34|cit34) 2004; 86 Lee (10.1002/sim.3854-BIB27|cit27) 2003; 290 Austin (10.1002/sim.3854-BIB8|cit8) 2008; 61 Austin (10.1002/sim.3854-BIB32|cit32) 2009; 28 Rosenbaum (10.1002/sim.3854-BIB11|cit11) 1987; 82 McCullagh (10.1002/sim.3854-BIB21|cit21) 1989 Jaeschke (10.1002/sim.3854-BIB3|cit3) 1995; 152 Austin (10.1002/sim.3854-BIB15|cit15) 2009; 51 Austin (10.1002/sim.3854-BIB23|cit23) Shah (10.1002/sim.3854-BIB20|cit20) 2005; 58 Agresti (10.1002/sim.3854-BIB17|cit17) 2004; 23 Rosenbaum (10.1002/sim.3854-BIB9|cit9) 1983; 70 Austin (10.1002/sim.3854-BIB16|cit16) 2009; 5 Rosenbaum (10.1002/sim.3854-BIB10|cit10) 1984; 79 Flury (10.1002/sim.3854-BIB29|cit29) 1986; 40 Austin (10.1002/sim.3854-BIB7|cit7) 2007; 26 Austin (10.1002/sim.3854-BIB13|cit13) 2007; 134 Sinclair (10.1002/sim.3854-BIB4|cit4) 1994; 47 Greenland (10.1002/sim.3854-BIB22|cit22) 1987; 125 Austin (10.1002/sim.3854-BIB26|cit26) 2007; 26 Austin (10.1002/sim.3854-BIB30|cit30) 2010; 63 Lunceford (10.1002/sim.3854-BIB18|cit18) 2004; 23 Austin (10.1002/sim.3854-BIB14|cit14) 2008; 1 10.1002/sim.3854-BIB28|cit28 Austin (10.1002/sim.3854-BIB24|cit24) 2008; 37 Austin (10.1002/sim.3854-BIB12|cit12) 2008; 27 Weitzen (10.1002/sim.3854-BIB19|cit19) 2004; 13 Cook (10.1002/sim.3854-BIB2|cit2) 1995; 310 Austin (10.1002/sim.3854-BIB31|cit31) 2009; 29 Austin (10.1002/sim.3854-BIB6|cit6) 2007; 26 Rubin (10.1002/sim.3854-BIB33|cit33) 2004; 13 Schechtman (10.1002/sim.3854-BIB1|cit1) 2002; 5 12201860 - Value Health. 2002 Sep-Oct;5(5):431-6 19197955 - Biom J. 2009 Feb;51(1):171-84 7730891 - J Clin Epidemiol. 1994 Aug;47(8):881-9 17976439 - J Thorac Cardiovasc Surg. 2007 Nov;134(5):1128-35 19757444 - Stat Med. 2009 Nov 10;28(25):3083-107 19716262 - J Clin Epidemiol. 2010 Feb;63(2):142-53 20031790 - Circ Cardiovasc Qual Outcomes. 2008 Sep;1(1):62-7 18038446 - Stat Med. 2008 May 30;27(12):2037-49 15386709 - Pharmacoepidemiol Drug Saf. 2004 Dec;13(12):841-53 15386710 - Pharmacoepidemiol Drug Saf. 2004 Dec;13(12):855-7 16220490 - Stat Med. 2006 Jun 30;25(12):2084-106 7828099 - CMAJ. 1995 Feb 1;152(3):351-7 15878468 - J Clin Epidemiol. 2005 Jun;58(6):550-9 18471657 - J Clin Epidemiol. 2008 Jun;61(6):537-45 15351954 - Stat Med. 2004 Oct 15;23(19):2937-60 14695640 - Stat Med. 2004 Jan 15;23(1):65-75 3551588 - Am J Epidemiol. 1987 May;125(5):761-8 19684288 - Med Decis Making. 2009 Nov-Dec;29(6):661-77 20949126 - Int J Biostat. 2009;5(1):Article 13 16783757 - Stat Med. 2007 Feb 20;26(4):754-68 16708349 - Stat Med. 2007 Feb 20;26(4):734-53 17187347 - Stat Med. 2007 Jul 20;26(16):3078-94 14625335 - JAMA. 2003 Nov 19;290(19):2581-7 7873954 - BMJ. 1995 Feb 18;310(6977):452-4 |
| References_xml | – reference: Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine 2009; 28:3083-3107. – reference: 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:Article 13. DOI: 10.2202/1557-4679.1146. – reference: 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; 152:351-357. – reference: 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; 29:661-677. – reference: Austin PC. The performance of different propensity score methods for estimating marginal odds ratios. Statistics in Medicine 2007; 26:3078-3094. – reference: Sinclair JC, Bracken MB. Clinically useful measures of effect in binary analyses of randomized trials. Journal of Clinical Epidemiology 1994; 47:881-889. – reference: Austin PC, Manca A, Zwarenstein M, Juurlink DN, Stanbrook MB. A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals. Journal of Clinical Epidemiology 2010; 63:142-153. DOI: 10.1016/j.jclinepi.2009.06.002. – reference: Austin PC. A report card on propensity-score matching in the cardiology literature from 2004-2006: results of a systematic review. Circulation: Cardiovascular Quality and Outcomes 2008; 1:62-67. – reference: Flury BK, Riedwyl H. Standard distance in univariate and multivariate analysis. The American Statistician 1986; 40:249-251. – reference: Rosenbaum PR. Model-based direct adjustment. The Journal of the American Statistician 1987; 82:387-394. – reference: Shah BR, Laupacis A, Hux JE, Austin PC. Propensity score methods give similar results to traditional regression modeling in observational studies: a systematic review. Journal of Clinical Epidemiology 2005; 58:550-559. – reference: McCullagh N, Nelder JA. Generalized Linear Models (2nd edn). Chapman & Hall: London, 1989. – reference: 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; 26:754-768. – reference: Austin PC. The performance of different propensity score methods for estimating relative risks. Journal of Clinical Epidemiology 2008; 61:537-545. – reference: Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine 2004; 23:2937-2960. – reference: Austin PC. Propensity-score matching in the cardiovascular surgery literature from 2004-2006: a systematic review and suggestions for improvement. Journal of Thoracic and Cardiovascular Surgery 2007; 134:1128-1135. – reference: Imbens GW. Nonparametric estimation of average treatment effects under exogeneity: a review. The Review of Economics and Statistics 2004; 86:4-29. – reference: Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70:41-55. – reference: Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 1984; 79:516-524. – reference: 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. Statistics in Medicine 2007; 26:734-753. – reference: Cook RJ, Sackett DL. The number needed to treat: a clinically useful measure of treatment effect. British Medical Journal 1995; 310:452-454. – reference: 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; 37:1039-1051. – reference: Austin PC. A critical appraisal of propensity score matching in the medical literature from 1996 to 2003. Statistics in Medicine 2008; 27:2037-2049. – reference: Weitzen S, Lapane KL, Toledano AY, Hume AL, Mor V. Principles for modeling propensity scores in medical research: a systematic literature review. Pharmacoepidemiology and Drug Safety 2004; 13:841-853. – reference: 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; 25:2084-2106. – reference: 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; 5:431-436. – reference: 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; 51:171-184. – reference: Agresti A, Min Y. Effects and non-effects of paired identical observations in comparing proportions with binary matched-pairs data. Statistics in Medicine 2004; 23:65-75. – reference: Austin PC. A data-generation process for data with specified risk differences or numbers needed to treat. Communications in Statistics-Simulation and Computation, in press. – reference: Greenland S. Interpretation and choice of effect measures in epidemiologic analyses. American Journal of Epidemiology 1987; 125:761-768. – reference: 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; 290:2581-2587. – reference: Rubin DB. On principles for modeling propensity scores in medical research. Pharmacoepidemiology and Drug Safety 2004; 13:885-887. – volume: 58 start-page: 550 year: 2005 end-page: 559 article-title: Propensity score methods give similar results to traditional regression modeling in observational studies: a systematic review publication-title: Journal of Clinical Epidemiology – volume: 40 start-page: 249 year: 1986 end-page: 251 article-title: Standard distance in univariate and multivariate analysis publication-title: The American Statistician – article-title: A data‐generation process for data with specified risk differences or numbers needed to treat publication-title: Communications in Statistics—Simulation and Computation – volume: 5 year: 2009 article-title: Type I error rates, coverage of confidence intervals, and variance estimation in propensity‐score matched analyses publication-title: The International Journal of Biostatistics – year: 1989 – volume: 290 start-page: 2581 year: 2003 end-page: 2587 article-title: Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model publication-title: Journal of the American Medical Association – volume: 70 start-page: 41 year: 1983 end-page: 55 article-title: The central role of the propensity score in observational studies for causal effects publication-title: Biometrika – volume: 25 start-page: 2084 year: 2006 end-page: 2106 article-title: A comparison of propensity score methods: a case‐study estimating the effectiveness of post‐AMI statin use publication-title: Statistics in Medicine – volume: 134 start-page: 1128 year: 2007 end-page: 1135 article-title: Propensity‐score matching in the cardiovascular surgery literature from 2004–2006: a systematic review and suggestions for improvement publication-title: Journal of Thoracic and Cardiovascular Surgery – volume: 37 start-page: 1039 year: 2008 end-page: 1051 article-title: The performance of two data‐generation processes for data with specified marginal treatment odds ratios publication-title: Communications in Statistics—Simulation and Computation – volume: 5 start-page: 431 year: 2002 end-page: 436 article-title: Odds ratio, relative risk, absolute risk reduction, and the number needed to treat—which of these should we use? publication-title: Value in Health – volume: 26 start-page: 754 year: 2007 end-page: 768 article-title: Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study publication-title: Statistics in Medicine – volume: 82 start-page: 387 year: 1987 end-page: 394 article-title: Model‐based direct adjustment publication-title: The Journal of the American Statistician – volume: 51 start-page: 171 year: 2009 end-page: 184 article-title: Some methods of propensity‐score matching had superior performance to others: results of an empirical investigation and Monte Carlo simulations publication-title: Biometrical Journal – volume: 23 start-page: 2937 year: 2004 end-page: 2960 article-title: Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study publication-title: Statistics in Medicine – volume: 23 start-page: 65 year: 2004 end-page: 75 article-title: Effects and non‐effects of paired identical observations in comparing proportions with binary matched–pairs data publication-title: Statistics in Medicine – volume: 47 start-page: 881 year: 1994 end-page: 889 article-title: Clinically useful measures of effect in binary analyses of randomized trials publication-title: Journal of Clinical Epidemiology – volume: 86 start-page: 4 year: 2004 end-page: 29 article-title: Nonparametric estimation of average treatment effects under exogeneity: a review publication-title: The Review of Economics and Statistics – year: 2008 – volume: 27 start-page: 2037 year: 2008 end-page: 2049 article-title: A critical appraisal of propensity score matching in the medical literature from 1996 to 2003 publication-title: Statistics in Medicine – year: 2004 – volume: 26 start-page: 734 year: 2007 end-page: 753 article-title: A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study publication-title: Statistics in Medicine – volume: 13 start-page: 841 year: 2004 end-page: 853 article-title: Principles for modeling propensity scores in medical research: a systematic literature review publication-title: Pharmacoepidemiology and Drug Safety – volume: 61 start-page: 537 year: 2008 end-page: 545 article-title: The performance of different propensity score methods for estimating relative risks publication-title: Journal of Clinical Epidemiology – volume: 1 start-page: 62 year: 2008 end-page: 67 article-title: A report card on propensity‐score matching in the cardiology literature from 2004–2006: results of a systematic review publication-title: Circulation: Cardiovascular Quality and Outcomes – volume: 125 start-page: 761 year: 1987 end-page: 768 article-title: Interpretation and choice of effect measures in epidemiologic analyses publication-title: American Journal of Epidemiology – volume: 13 start-page: 885 year: 2004 end-page: 887 article-title: On principles for modeling propensity scores in medical research publication-title: Pharmacoepidemiology and Drug Safety – volume: 28 start-page: 3083 year: 2009 end-page: 3107 article-title: Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity‐score matched samples publication-title: Statistics in Medicine – volume: 29 start-page: 661 year: 2009 end-page: 677 article-title: The relative ability of different propensity‐score methods to balance measured covariates between treated and untreated subjects in observational studies publication-title: Medical Decision Making – volume: 63 start-page: 142 year: 2010 end-page: 153 article-title: A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals publication-title: Journal of Clinical Epidemiology – volume: 310 start-page: 452 year: 1995 end-page: 454 article-title: The number needed to treat: a clinically useful measure of treatment effect publication-title: British Medical Journal – volume: 152 start-page: 351 year: 1995 end-page: 357 article-title: Basis statistics for clinicians 3: assessing the effects of treatment: measures of association publication-title: Canadian Medical Association Journal – volume: 26 start-page: 3078 year: 2007 end-page: 3094 article-title: The performance of different propensity score methods for estimating marginal odds ratios publication-title: Statistics in Medicine – volume: 79 start-page: 516 year: 1984 end-page: 524 article-title: Reducing bias in observational studies using subclassification on the propensity score publication-title: Journal of the American Statistical 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| SubjectTerms | Adrenergic beta-Antagonists - therapeutic use Analysis of Variance Bias binary data Biostatistics Clinical outcomes Confidence Intervals Databases, Factual Differences Heart Failure - drug therapy Heart Failure - mortality Humans inverse probability of treatment weighting IPTW matching Medical statistics Models, Statistical Monte Carlo Method Monte Carlo simulation number needed to treat observational study Odds Ratio Propensity Score propensity-score matching Risk Risk assessment risk difference |
| Title | The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies |
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