Estimating Propensity Scores and Causal Survival Functions Using Prevalent Survival Data

Yu Jen Cheng, Mei Cheng Wang

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)707-716
Number of pages10
Issue number3
StatePublished - Sep 2012


  • Case-control study
  • Prevalent sampling
  • Propensity scores

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


Dive into the research topics of 'Estimating Propensity Scores and Causal Survival Functions Using Prevalent Survival Data'. Together they form a unique fingerprint.

Cite this