A comparative study of parametric and nonparametric estimates of the attributable fraction for a semi-continuous exposure

Abstract

The attributable fraction of a disease due to an exposure is the fraction of disease cases in a population that can be attributed to that exposure. We consider the attributable fraction for a semi-continuous exposure, that is an exposure for which a clump of people have zero exposure and the rest of the people have a continuously distributed positive exposure. Estimation of the attributable fraction involves estimating the conditional probability of having the disease given the exposure. Three main approaches to estimating the probability function are (1) a classical method based on sample averages; (2) parametric regression methods such as logistic regression models and power models; and (3) nonparametric regression methods including local linear smoothing and isotonic regression. We compare performance of these methods in estimating the attributable fraction for a semi-continuous exposure in a simulation study and in an example.