Estimating generalized propensity scores with survey and attrition weighted data
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Wiley
2024
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| author | Daniel F. McCaffrey Beth Ann Griffin Michael Robbins Yajnaseni Chakraborti Donna L. Coffman Brian Vegetabile |
| author_facet | Daniel F. McCaffrey Beth Ann Griffin Michael Robbins Yajnaseni Chakraborti Donna L. Coffman Brian Vegetabile Daniel F. McCaffrey Beth Ann Griffin Michael Robbins Yajnaseni Chakraborti Donna L. Coffman Brian Vegetabile |
| collection | Wiley Open Access |
| contents | Estimating generalized propensity scores with survey and attrition weighted data Daniel F. McCaffrey Beth Ann Griffin Michael Robbins Yajnaseni Chakraborti Donna L. Coffman Brian Vegetabile Statistics in Medicine Prior work in causal inference has shown that using survey sampling weights in the propensity score estimation stage and the outcome model stage for binary treatments can result in a more robust estimator of the effect of the binary treatment being analyzed. However, to date, extending this work to continuous treatments and exposures has not been explored nor has consideration been given for how to handle attrition weights in the propensity score model. Nonetheless, generalized propensity score (GPS) analyses are being used for estimating continuous treatment effects on outcomes when researchers have observational data, and those data sets often have survey or attrition weights that need to be accounted for in the analysis. Here, we extend prior work and show with analytic results that using survey sampling or attrition weights in the GPS estimation stage and the outcome model stage for continuous treatments can result in a more robust estimator than one that does not. Simulation study results show that, although using weights in both estimation stages is sufficient for robust estimation, it is not necessary and unbiased estimation is possible in some cases under various approaches to using weights in estimation. Analysts do not know if the conditions of our simulation studies hold, so use of weights in both estimation stages might provide insurance for reducing potential bias. We discuss the implications of our results in the context of an empirical example. 10.1002/sim.10039 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/sim.10039 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_10039 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Estimating generalized propensity scores with survey and attrition weighted data Daniel F. McCaffrey Beth Ann Griffin Michael Robbins Yajnaseni Chakraborti Donna L. Coffman Brian Vegetabile Statistics in Medicine Estimating generalized propensity scores with survey and attrition weighted data Daniel F. McCaffrey Beth Ann Griffin Michael Robbins Yajnaseni Chakraborti Donna L. Coffman Brian Vegetabile Statistics in Medicine Prior work in causal inference has shown that using survey sampling weights in the propensity score estimation stage and the outcome model stage for binary treatments can result in a more robust estimator of the effect of the binary treatment being analyzed. However, to date, extending this work to continuous treatments and exposures has not been explored nor has consideration been given for how to handle attrition weights in the propensity score model. Nonetheless, generalized propensity score (GPS) analyses are being used for estimating continuous treatment effects on outcomes when researchers have observational data, and those data sets often have survey or attrition weights that need to be accounted for in the analysis. Here, we extend prior work and show with analytic results that using survey sampling or attrition weights in the GPS estimation stage and the outcome model stage for continuous treatments can result in a more robust estimator than one that does not. Simulation study results show that, although using weights in both estimation stages is sufficient for robust estimation, it is not necessary and unbiased estimation is possible in some cases under various approaches to using weights in estimation. Analysts do not know if the conditions of our simulation studies hold, so use of weights in both estimation stages might provide insurance for reducing potential bias. We discuss the implications of our results in the context of an empirical example. 10.1002/sim.10039 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Estimating generalized propensity scores with survey and attrition weighted data |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.10039 |