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Activity Number: 45 - Nonparametric and Semiparametric Modeling for Complex Lifetime Data
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Lifetime Data Science Section
Abstract #313809
Title: Non-Parametric Estimation in an Illness-Death Model with Component-Wise Censoring
Author(s): Anne Eaton* and Yifei Sun and Jim Neaton and Xianghua Luo
Companies: University of Minnesota and Mailman School of Public Health, Columbia University and University of Minnesota and University of Minnesota
Keywords: composite endpoint; survival analysis; non-parametric; kernel smoothing; censoring
Abstract:

When a composite endpoint consists of death and a non-fatal event, if the non-fatal event can only be detected at clinic visits, the resulting composite endpoint exhibits "component-wise censoring". The method recommended by the FDA to estimate event-free survival for this type of data fails to account for component-wise censoring. We apply a kernel method previously proposed for a marker process in a novel way to produce a non-parametric estimator that accounts for component-wise censoring. The key insight that allows us to apply this method is thinking of non-fatal event status as an intermittently observed, binary marker variable rather than thinking of time to the non-fatal event as interval censored. We also obtain estimates of the probability of being alive with the non-fatal event, and the restricted mean time patients spend in disease states. The method can be used in the setting of reversible non-fatal events. We perform a simulation study to compare our method to existing multistate survival methods and apply the methods on data from a large randomized trial studying interventions for reducing the risk of coronary heart disease.


Authors who are presenting talks have a * after their name.

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