Estimating attributable risk functions for censored time-to-event in disease prevention research.

Abstract

In disease prevention research, researchers often need to assess a prevention strategy that targets key disease-associated risk factors to reduce a population's disease burden. In this article, the fraction of the total disease burden associated with the risk factors targeted by the prevention strategy is calculated by time-varying attributable risk functions (ARF) when the disease outcome is censored time-to-event. We study some generic ARFs and develop nonparametric and semiparametric model-based procedures to estimate, compare, and predict ARFs. In addition to numerical simulation studies, we demonstrate the use of ARFs for a human immunodeficiency virus (HIV) behavior intervention trial in prevention of HIV transmissions among men who have sex with men (MSM).