Nonparametric Estimation for Event-Free Survival with Component-Wise Censoring (306495)*Anne Eaton, University of Minnesota
Xianghua Luo, University of Minnesota
Jim Neaton, University of Minnesota
Yifei Sun , Columbia University
Keywords: survival analysis, interval censoring, progression free survival, kernel smoothing
In diseases where patients are at risk for death and a serious non-fatal event, a composite endpoint defined as the time until the earliest of death or the non-fatal event can be used to measure prognosis. If the non-fatal event can only be detected at clinic visits, it is interval censored, while date of death is usually known exactly, leading to "component-wise censoring". The standard method to estimate event-free survival for this type of data fails to account for component-wise censoring. We apply an existing non-parametric method (referred to as the Sun et al method) in a novel way to produce unbiased estimates of event-free survival, and use simulations to compare this method to the FDA method and a parametric method. The Sun et al method performs well if patients' visit schedule is independent of their non-fatal event status. We propose an estimator that relaxes this assumption, and explain the intuition behind it. Finally, we illustrate the methods on data from the MRFIT trial, which tested a multifactor intervention aimed at lowering cholesterol and blood pressure to reduce coronary heart disease, using the composite endpoint cardiovascular event free survival.