Anne Eaton, of the Division of Biostatistics at the University of Minnesota and candidate for faculty position in the Division of Biostatistics, Biostatistics Core of the Masonic Cancer Center, will present:

“Non-parametric Estimation in an Illness-death Model with Component-wise Censoring”

Abstract: In disease settings where patients are at risk for death and a serious non-fatal event, composite endpoints defined as the time until the earliest of death or the non-fatal event are often used as the primary endpoint in clinical trials. In practice, if the non-fatal event can only be detected at clinic visits and the death time is known exactly, 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 kernel 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 or irreversible 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 in high-risk men.

A social tea will be held at 3:00 p.m. in A434 Mayo. All are Welcome.