Comparison of several imputation methods for missing baseline data in propensity scores analysis of binary outcome
We performed a simulation study comparing the statistical properties of the estimated log odds ratio from propensity scores analyses of a binary response variable, in which missing baseline data had been imputed using a simple imputation scheme (Treatment Mean Imputation), compared with three ways o...
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| Published in: | Pharmaceutical statistics : the journal of the pharmaceutical industry Vol. 9; no. 4; pp. 269 - 279 |
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| Abstract | We performed a simulation study comparing the statistical properties of the estimated log odds ratio from propensity scores analyses of a binary response variable, in which missing baseline data had been imputed using a simple imputation scheme (Treatment Mean Imputation), compared with three ways of performing multiple imputation (MI) and with a Complete Case analysis. MI that included treatment (treated/untreated) and outcome (for our analyses, outcome was adverse event [yes/no]) in the imputer's model had the best statistical properties of the imputation schemes we studied. MI is feasible to use in situations where one has just a few outcomes to analyze. We also found that Treatment Mean Imputation performed quite well and is a reasonable alternative to MI in situations where it is not feasible to use MI. Treatment Mean Imputation performed better than MI methods that did not include both the treatment and outcome in the imputer's model. Copyright © 2009 John Wiley & Sons, Ltd. |
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| AbstractList | We performed a simulation study comparing the statistical properties of the estimated log odds ratio from propensity scores analyses of a binary response variable, in which missing baseline data had been imputed using a simple imputation scheme (Treatment Mean Imputation), compared with three ways of performing multiple imputation (MI) and with a Complete Case analysis. MI that included treatment (treated/untreated) and outcome (for our analyses, outcome was adverse event [yes/no]) in the imputer's model had the best statistical properties of the imputation schemes we studied. MI is feasible to use in situations where one has just a few outcomes to analyze. We also found that Treatment Mean Imputation performed quite well and is a reasonable alternative to MI in situations where it is not feasible to use MI. Treatment Mean Imputation performed better than MI methods that did not include both the treatment and outcome in the imputer's model. Copyright © 2009 John Wiley & Sons, Ltd. We performed a simulation study comparing the statistical properties of the estimated log odds ratio from propensity scores analyses of a binary response variable, in which missing baseline data had been imputed using a simple imputation scheme (Treatment Mean Imputation), compared with three ways of performing multiple imputation (MI) and with a Complete Case analysis. MI that included treatment (treated/untreated) and outcome (for our analyses, outcome was adverse event [yes/no]) in the imputer's model had the best statistical properties of the imputation schemes we studied. MI is feasible to use in situations where one has just a few outcomes to analyze. We also found that Treatment Mean Imputation performed quite well and is a reasonable alternative to MI in situations where it is not feasible to use MI. Treatment Mean Imputation performed better than MI methods that did not include both the treatment and outcome in the imputer's model. |
| Author | Wang, Ouhong Crowe, Brenda J. Lipkovich, Ilya A. |
| Author_xml | – sequence: 1 givenname: Brenda J. surname: Crowe fullname: Crowe, Brenda J. email: crowe_brenda_j@lilly.com organization: Eli Lilly and Company, Indianapolis, IN, USA – sequence: 2 givenname: Ilya A. surname: Lipkovich fullname: Lipkovich, Ilya A. organization: Eli Lilly and Company, Indianapolis, IN, USA – sequence: 3 givenname: Ouhong surname: Wang fullname: Wang, Ouhong organization: Amgen, Thousand Oaks, CA, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19718652$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.2307/2531052 10.2307/2288398 10.1201/9781439821862 10.2307/2669455 10.1002/9780470316696 10.1111/j.1751-5823.2003.tb00214.x 10.1093/biomet/85.4.935 10.1214/ss/1177010269 10.1093/biomet/70.1.41 10.1002/9781119013563 10.2307/2290664 10.1093/biomet/80.2.267 10.1111/1467-9868.00170 10.1093/biomet/87.1.113 10.1023/A:1020375413191 10.1023/A:1020363010465 10.1002/sim.2739 10.1080/01621459.1996.10476908 10.1023/A:1020327530029 |
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| References_xml | – volume: 71 start-page: 593 year: 2003 end-page: 607 article-title: Proper and improper multiple imputation publication-title: International Statistical Review – volume: 87 start-page: 1227 year: 1992 end-page: 1237 article-title: Regression with missing X's: a review publication-title: American Statistical Association – volume: 2 start-page: 169 year: 2001 end-page: 188 article-title: Using propensity scores to help design observational studies: application to the tobacco litigation publication-title: Health Services & Outcomes Research Methodology – volume: 61 start-page: 173 year: 1999 end-page: 190 article-title: Missing covariates in generalized linear models when the missing data mechanism is nonignorable publication-title: Journal of the Royal Statistical Society Series B (Statistical Methodology) – volume: 42 start-page: 311 year: 1986 end-page: 323 article-title: Estimators of the Mantel–Haenszel variance consistent in both sparse data and large‐strata limiting models publication-title: Biometrics – volume: 95 start-page: 749 year: 2000 end-page: 759 article-title: Estimating and using propensity scores with partially missing data publication-title: Journal of the American Statistical Association – volume: 2 start-page: 291 year: 2001 end-page: 315 article-title: Examining the impact of missing data on propensity score estimation in determining the effectiveness of self‐monitoring of blood glucose (SMBG) 2001 publication-title: Health Services & Outcomes Research Methodology – volume: 2 start-page: 317 year: 2001 end-page: 329 article-title: Handling baseline differences and missing items in a longitudinal study of HIV risk among runaway youths publication-title: Health Services & Outcomes Research Methodology – year: 2002 – volume: 70 start-page: 41 issue: 1 year: 1983 end-page: 55 article-title: The central role of the propensity score in observational studies for causal effects publication-title: Biometrika – year: 1987 – year: 2003 – volume: 85 start-page: 935 year: 1998 end-page: 948 article-title: Large‐sample theory for parametric multiple imputation procedures publication-title: Biometrika – year: 1997 – volume: 91 start-page: 473 year: 1996 end-page: 489 article-title: Multiple imputation after 18+years publication-title: JASA – start-page: 145 end-page: 146 – start-page: 20 end-page: 34 – start-page: 227 end-page: 232 – volume: 26 start-page: 20 year: 2007 end-page: 36 article-title: The design versus the analysis of observational studies for causal effects: Parallels with the design of randomized trials publication-title: Statistics in Medicine – volume: 22 start-page: 719 year: 1959 end-page: 748 article-title: Statistical aspects of the analysis of data from retrospective studies of disease publication-title: Journal of the National Cancer Institute – volume: 9 start-page: 538 year: 1994 end-page: 574 article-title: Multiple imputation with uncongenial sources of input (with discussion) publication-title: Statistical Science – volume: 79 start-page: 516 year: 1984 end-page: 524 article-title: Reducing bias in observation studies using subclassification on the propensity score publication-title: Journal of the American Statistical Association – volume: 87 start-page: 113 year: 2000 end-page: 124 article-title: Inference for imputation estimators publication-title: Biometrika – volume: 80 start-page: 267 year: 1993 end-page: 278 article-title: Maximum likelihood estimation via the ECM algorithm: a general framework publication-title: Biometrika – volume: 42 start-page: 311 year: 1986 ident: 10.1002/pst.389-BIB5|cit5 article-title: Estimators of the Mantel-Haenszel variance consistent in both sparse data and large-strata limiting models publication-title: Biometrics doi: 10.2307/2531052 contributor: fullname: Robins – volume: 79 start-page: 516 year: 1984 ident: 10.1002/pst.389-BIB2|cit2 article-title: Reducing bias in observation studies using subclassification on the propensity score publication-title: Journal of the American Statistical Association doi: 10.2307/2288398 contributor: fullname: Rosenbaum – volume-title: Analysis of incomplete multivariate data year: 1997 ident: 10.1002/pst.389-BIB8|cit8 doi: 10.1201/9781439821862 contributor: fullname: Schafer – volume: 95 start-page: 749 year: 2000 ident: 10.1002/pst.389-BIB6|cit6 article-title: Estimating and using propensity scores with partially missing data publication-title: Journal of the American Statistical Association doi: 10.2307/2669455 contributor: fullname: D'Agostino – volume-title: Multiple imputation for nonresponse in surveys year: 1987 ident: 10.1002/pst.389-BIB13|cit13 doi: 10.1002/9780470316696 contributor: fullname: Rubin – volume: 71 start-page: 593 year: 2003 ident: 10.1002/pst.389-BIB18|cit18 article-title: Proper and improper multiple imputation publication-title: International Statistical Review doi: 10.1111/j.1751-5823.2003.tb00214.x contributor: fullname: Nielson – volume: 85 start-page: 935 year: 1998 ident: 10.1002/pst.389-BIB20|cit20 article-title: Large-sample theory for parametric multiple imputation procedures publication-title: Biometrika doi: 10.1093/biomet/85.4.935 contributor: fullname: Wang – volume: 9 start-page: 538 year: 1994 ident: 10.1002/pst.389-BIB15|cit15 article-title: Multiple imputation with uncongenial sources of input (with discussion) publication-title: Statistical Science doi: 10.1214/ss/1177010269 contributor: fullname: Meng – ident: 10.1002/pst.389-BIB17|cit17 – volume: 70 start-page: 41 issue: 1 year: 1983 ident: 10.1002/pst.389-BIB1|cit1 article-title: The central role of the propensity score in observational studies for causal effects publication-title: Biometrika doi: 10.1093/biomet/70.1.41 contributor: fullname: Rosenbaum – volume-title: Statistical analysis with missing data year: 2002 ident: 10.1002/pst.389-BIB11|cit11 doi: 10.1002/9781119013563 contributor: fullname: Little – volume: 87 start-page: 1227 year: 1992 ident: 10.1002/pst.389-BIB22|cit22 article-title: Regression with missing X's: a review publication-title: American Statistical Association doi: 10.2307/2290664 contributor: fullname: Little – volume: 80 start-page: 267 year: 1993 ident: 10.1002/pst.389-BIB7|cit7 article-title: Maximum likelihood estimation via the ECM algorithm: a general framework publication-title: Biometrika doi: 10.1093/biomet/80.2.267 contributor: fullname: Meng – volume: 61 start-page: 173 year: 1999 ident: 10.1002/pst.389-BIB10|cit10 article-title: Missing covariates in generalized linear models when the missing data mechanism is nonignorable publication-title: Journal of the Royal Statistical Society Series B (Statistical Methodology) doi: 10.1111/1467-9868.00170 contributor: fullname: Ibrahim – volume: 87 start-page: 113 year: 2000 ident: 10.1002/pst.389-BIB19|cit19 article-title: Inference for imputation estimators publication-title: Biometrika doi: 10.1093/biomet/87.1.113 contributor: fullname: Robins – volume: 2 start-page: 291 year: 2001 ident: 10.1002/pst.389-BIB9|cit9 article-title: Examining the impact of missing data on propensity score estimation in determining the effectiveness of self-monitoring of blood glucose (SMBG) 2001 publication-title: Health Services & Outcomes Research Methodology doi: 10.1023/A:1020375413191 contributor: fullname: D'Agostino – volume: 2 start-page: 169 year: 2001 ident: 10.1002/pst.389-BIB23|cit23 article-title: Using propensity scores to help design observational studies: application to the tobacco litigation publication-title: Health Services & Outcomes Research Methodology doi: 10.1023/A:1020363010465 contributor: fullname: Rubin – volume: 22 start-page: 719 year: 1959 ident: 10.1002/pst.389-BIB4|cit4 article-title: Statistical aspects of the analysis of data from retrospective studies of disease publication-title: Journal of the National Cancer Institute contributor: fullname: Mantel – volume: 26 start-page: 20 year: 2007 ident: 10.1002/pst.389-BIB24|cit24 article-title: The design versus the analysis of observational studies for causal effects: Parallels with the design of randomized trials publication-title: Statistics in Medicine doi: 10.1002/sim.2739 contributor: fullname: Rubin – volume-title: SAS/STAT® User's Guide, Version 9.1 year: 2003 ident: 10.1002/pst.389-BIB14|cit14 contributor: fullname: SAS Institute Inc – ident: 10.1002/pst.389-BIB3|cit3 – volume: 91 start-page: 473 year: 1996 ident: 10.1002/pst.389-BIB16|cit16 article-title: Multiple imputation after 18+years publication-title: JASA doi: 10.1080/01621459.1996.10476908 contributor: fullname: Rubin – ident: 10.1002/pst.389-BIB12|cit12 – volume: 2 start-page: 317 year: 2001 ident: 10.1002/pst.389-BIB21|cit21 article-title: Handling baseline differences and missing items in a longitudinal study of HIV risk among runaway youths publication-title: Health Services & Outcomes Research Methodology doi: 10.1023/A:1020327530029 contributor: fullname: 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| Title | Comparison of several imputation methods for missing baseline data in propensity scores analysis of binary outcome |
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