A Semiparametric Regression Method for Interval-Censored Data

Model evaluation based on the negative predictive value for interval-censored survival outcomes

In many cohort studies, time to events such as disease recurrence is recorded in an interval-censored format. An important objective is to predict patient outcomes. Clinicians are interested in predictive covariates. Prediction rules based on the receiver operating characteristic curve alone are not related to the survival endpoint. We propose a model evaluation strategy to leverage the predictive accuracy based on negative predictive functions. Our proposed method makes very few assumptions and only requires a working model to obtain the regression coefficients. A nonparametric estimate of the predictive accuracy provides a simple and flexible approach for model evaluation to interval-censored survival outcomes. The implementation effort is minimal, therefore this method has an increased potential for immediate use in biomedical data analyses. Simulation studies and a breast cancer trial example further illustrate the practical advantages of this approach.