Regulatory Considerations in the Design of Comparative Observational Studies Using Propensity Scores

REFERENCES

• Braitman , L. , Rosenbaum , P. R. ( 2002 ). Rare outcomes, common treatments: Analytic strategies using propensity scores . Annals of Internal Medicine 137 : 693 – 696 .
   PubMed Web of Science ®Google Scholar
• Brookhart , M. A. , Schneeweiss , S. , Rothman , K. J. , Glynn , R. J. , Avorn , J. , Sturmer , T. ( 2006 ). Variable selection for propensity score models: American Journal of Epidemiology 163:1149–1156 .
   Google Scholar
• D'Agostino , R. B. , Jr. ( 1998 ). Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group . Statistics in Medicine 17 : 2265 – 2281 .
   PubMed Web of Science ®Google Scholar
• D'Agostino , R. B. , Jr. , Rubin , D. B. ( 2000 ). Estimating and using propensity scores with partially missing data . Journal of the American Statistical Association 95 : 749 – 759 .
   Web of Science ®Google Scholar
• Hill , J. , Reiter , J. ( 2006 ). Interval estimation for treatment effects using propensity score matching . Statistics in Medicine 25 : 2230 – 2256 .
   PubMed Web of Science ®Google Scholar
• Li , H. , Yue , L.Q. ( 2008 ). Statistical and regulatory issues in non-randomized medical device clinical studies . Journal of Biopharmaceutical Statistics 18 : 20 – 30 .
   PubMed Web of Science ®Google Scholar
• Lunceford , J. K. , Davidian , M. ( 2004 ). Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study . Statistics in Medicine 23 : 2937 – 2960 .
   PubMed Web of Science ®Google Scholar
• Qu , Y. , Lipkovich , I. ( 2009 ). Propensity score estimation with missing values using a multiple imputation missingness pattern (IMP) approach . Statistics in Medicine 28 : 1402 – 1414 .
   PubMed Web of Science ®Google Scholar
• Rosenbaum , P. R. ( 2005 ). Propensity score . In: Armitage , P. , Colton , T. , eds. Encyclopedia of Biostatistics . 2nd ed. England : Wiley , pp. 4267 – 4272 .
   Google Scholar
• Rosenbaum , P. R. , Rubin D. B. ( 1983 ). The central role of the propensity score in observational studies for causal effects . Biometrika 70 : 41 – 55 .
   Web of Science ®Google Scholar
• Rosenbaum , P. R. , Rubin , D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. JASA 1984; 79:516–524.
   Google Scholar
• Rubin , D. B. ( 1997 ). Estimating casual effects from large data sets using propensity scores . Annals of Internal Medicine 127 : 757 – 763 .
   PubMed Web of Science ®Google Scholar
• Rubin , D. B. ( 2001 ). Using propensity score to help design observational studies: Application to the tobacco litigation . Health Services and Outcomes Research Methodology 2 : 169 – 188 .
   Google Scholar
• Rubin , D. B. ( 2007 ). The design versus the analysis of observational studies for causal effects: Parallels with the design of randomized trials . Statistics in Medicine 26 : 20 – 36 .
   PubMed Web of Science ®Google Scholar
• Rubin , D. B. ( 2008 ). For objective causal inference, design trumps analysis . Annals of Applied Statistics 2 ( 3 ): 808 – 840 .
   Web of Science ®Google Scholar
• Yue , L. Q. ( 2007 ). Statistical and regulatory issues with the application of propensity score analysis to nonrandomized medical device clinical studies . Journal of Biopharmaceutical Statistics 17 : 1 – 13 .
   PubMed Web of Science ®Google Scholar
• This article is not subject to US copyright law.
   Google Scholar
• No official support or endorsement by the Food and Drug Administration of this presentation is intended or should be inferred.
   Google Scholar