Nonparametric Estimation of Event-Free Survival for Data with Left-Truncated Death and Intermittently Assessed Nonfatal Events

Thomas M. Menino Convention & Exhibition Center 

Left-truncated time-to-event data are frequently encountered in health sciences research, e.g., epidemiologic studies of prevalent cohorts. In certain clinical settings, patients are at risk for death as well as a serious but nonfatal event, and a composite endpoint, defined as the time from disease onset until the earlier of the nonfatal event and death, might be desirable. Component-wise censoring of a composite endpoint arises when each component is subject to a different censoring mechanism. For example, the nonfatal event may be interval censored between visits for assessments, and death is right censored. Methods to estimate the event-free survival (EFS) with left-truncated and right-censored data are available in the literature, but they cannot handle component-wise censoring. We propose a kernel smoothing method for left-truncated and component-wise-censored data to provide a nonparametric estimator for EFS, which takes component-wise censoring into account by treating the nonfatal event as a time-dependent binary variable that is observed intermittently during follow-up. Our method can also estimate and test for differences in the restricted mean EFS time and can leverage two types of supplemental data that may be available in the same study: death-only data (if some participants are only followed for death but not for the nonfatal event) and incident cohort data (if a study also enrolls participants who have not yet experienced the index event at the enrollment time). We assess the proposed methods by simulations of different study designs and demonstrate the method using the Atherosclerosis Risk in Communities Study to estimate post-myocardial infarction, dementia-free survival probability.

Component-wise censoring

Composite endpoint

Event-free survival

Kernel estimation

Restricted mean survival time

Survival analysis