A biomarker-augmented regression model for left- and interval-censored outcomes.

Thomas M. Menino Convention & Exhibition Center 

In survival analysis, longitudinal data related to a time-to-event outcome are often incorporated into regression and prediction frameworks. Depending on data quality, these measures may be treated as time-varying covariates or modeled through a longitudinal sub-model within a joint modeling framework. Classic examples include longitudinal CD4 counts in studies of HIV disease progression or repeated PHQ assessments in studies of depression recovery. In this work, we extend these ideas to the semiparametric framework for interval-censored time-to-event data, where only the interval of an event is observed rather than the exact event time. Rather than using longitudinal biomarkers solely as predictors of event time, we investigate their role in improving estimation of nuisance parameters—such as the hazard function—and, in turn, enhancing the accuracy of cumulative incidence curve estimation. Our approach also enables estimation of biomarker distributions in both healthy and diseased populations. We will present results from a comprehensive simulation study and demonstrate the utility of our proposed biomarker-augmented method through an application to a tuberculosis (TB) study among people living with HIV.

Augmented likelihood

Heterogeneous biomarker distributions

Interval-censoring

Measurement error