Estimating Propensity Scores and Causal Survival Functions Using Prevalent Survival Data
Article first published online: 26 JUL 2012
© 2012, The International Biometric Society
Volume 68, Issue 3, pages 707–716, September 2012
How to Cite
Cheng, Y.-J. and Wang, M.-C. (2012), Estimating Propensity Scores and Causal Survival Functions Using Prevalent Survival Data. Biometrics, 68: 707–716. doi: 10.1111/j.1541-0420.2012.01754.x
- Issue published online: 26 SEP 2012
- Article first published online: 26 JUL 2012
- Received February 2011. Revised January 2012. Accepted January 2012.
- Case-control study;
- Prevalent sampling;
- Propensity scores
Summary This article develops semiparametric approaches for estimation of propensity scores and causal survival functions from prevalent survival data. The analytical problem arises when the prevalent sampling is adopted for collecting failure times and, as a result, the covariates are incompletely observed due to their association with failure time. The proposed procedure for estimating propensity scores shares interesting features similar to the likelihood formulation in case-control study, but in our case it requires additional consideration in the intercept term. The result shows that the corrected propensity scores in logistic regression setting can be obtained through standard estimation procedure with specific adjustments on the intercept term. For causal estimation, two different types of missing sources are encountered in our model: one can be explained by potential outcome framework; the other is caused by the prevalent sampling scheme. Statistical analysis without adjusting bias from both sources of missingness will lead to biased results in causal inference. The proposed methods were partly motivated by and applied to the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data for women diagnosed with breast cancer.