Article: Variance reduction in the inverse probability weighted estimators for the average treatment effect using the propensity score
Biometrics. 2022 June, v. 78, no. 2
2022
Abstract: The propensity methodology is widely used in medical research to compare different treatments in designs with a nonrandomized treatment allocation. The inverse probability weighted (IPW) estimators are a primary tool for estimating the average treatment ... ...
Abstract | The propensity methodology is widely used in medical research to compare different treatments in designs with a nonrandomized treatment allocation. The inverse probability weighted (IPW) estimators are a primary tool for estimating the average treatment effect but the large variance of these estimators is often a significant concern for their reliable use in practice. Inspired by Rao‐Blackwellization, this paper proposes a method to smooth an IPW estimator by replacing the weights in the original estimator by their mean over a distribution of the potential treatment assignment. In our simulation study, the smoothed IPW estimator achieves a substantial variance reduction over its original version with only a small increased bias, for example two‐to‐sevenfold variance reduction for the three IPW estimators in Lunceford and Davidian [Statistics in Medicine, 23(19), 2937–2960]. In addition, our proposed smoothing can also be applied to the locally efficient and doubly robust estimator for added protection against model misspecification. An implementation in R is provided. |
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Keywords | biomedical research ; medicine ; probability ; variance |
Language | English |
Dates of publication | 2022-06 |
Size | p. 660-667. |
Publishing place | John Wiley & Sons, Ltd |
Document type | Article |
Note | JOURNAL ARTICLE |
ZDB-ID | 213543-7 |
ISSN | 0099-4987 ; 0006-341X |
ISSN | 0099-4987 ; 0006-341X |
DOI | 10.1111/biom.13454 |
Database | NAL-Catalogue (AGRICOLA) |
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