Title: Classification of unknown cause of failure in competing risks: An application to recurrence of P.vivax malaria infection
Authors: Yutong Liu - University of North Carolina - Chapel Hill (United States) [presenting]
Feng-Chang Lin - University of North Carolina at Chapel Hill (United States)
Jessica Lin - University of North Carolina at Chapel Hill (United States)
Quefeng Li - University of North Carolina - Chapel Hill (United States)
Abstract: A standard competing risks set-up requires both time-to-event and cause of failure to be fully observable for all subjects. However, in applications, the cause of failure may not always be observable, impeding the risk assessment. In some extreme cases, none of the causes of failure is observable. In the case of a recurrent episode of Plasmodium vivax malaria following treatment, the patient may have suffered a relapse from a previous infection or acquired a new infection from a mosquito bite. In this case, the time to relapse cannot be modeled when a competing risk, a new infection, is present. The efficacy of a treatment for preventing relapse from a previous infection may be underestimated when the true cause of infection cannot be classified. We developed a novel method for classifying the latent cause of failure under a competing risks set-up, which uses not only time to event information but also transition likelihoods between covariates at the baseline and at the time of event occurrence. Our classifier shows superior performance under various scenarios of simulation experiments. The method was applied to Plasmodium vivax infection data to classify recurrent malaria infections.