Bias-adjusted Kaplan-Meier survival curves for marginal treatment effect in observational studies.

ABSTRACT: For time-to-event outcomes, the Kaplan-Meier estimator is commonly used to estimate survival functions of treatment groups and to compute marginal treatment effects, such as the difference in survival rates between treatments at a landmark time. The derived estimates of the marginal treatment effect are uniformly consistent under general conditions when data are from randomized clinical trials. For data from observational studies, however, these statistical quantities are often biased due to treatment-selection bias. Propensity score-based methods estimate the survival function by adjusting for the disparity of propensity scores between treatment groups. Unfortunately, misspecification of the regression model can lead to biased estimates. Using an empirical likelihood (EL) method in which

SUBMITTER: Wang X 

PROVIDER: S-EPMC6684083 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature