Estimating treatment effects in observational studies with both prevalent and incident cohorts

Registry databases are increasingly being used for comparative effectiveness research in cancer. Such databases reflect the real-world patient population and physician practice, and thus are natural sources for comparing multiple treatment scenarios and the associated long-term clinical outcomes. Registry databases usually include both incident and prevalent cohorts, which provide valuable complementary information for patients with more recent diagnoses in the incident cohort as well as patients with long-term follow-up data in the prevalent cohort. However, utilizing such data to derive valid inference poses two major challenges: the data from a prevalent cohort are not random samples of the target population, and there may be substantial differences in the baseline characteristics of patients between treatment arms, which influences the decisions about treatment selection in both cohorts. In this article we extend propensity score methodology to observational studies that involve both prevalent and incident cohorts, and assess the effectiveness of radiation therapy (RT) in SEER-Medicare patients diagnosed with stage IV breast cancer. Specifically we utilize the incident cohort to estimate the propensity for receiving RT, and then combine data from both the incident and prevalent cohorts to estimate the effect of RT by adjusting for the propensity scores in the model. We evaluate the proposed method with simulations. We demonstrate that the proposed propensity score method simultaneously removes sampling bias and selection bias under several assumptions. The Canadian Journal of Statistics 45: 202–219; 2017 © 2017 Statistical Society of Canada.

Duke Scholars

Author Chuan Hong Biostatistics & Bioinformatics, Division of Translational Bi ...