Semiparametric estimation of the attributable fraction when there are interactions under monotonicity constraints.

ABSTRACT:

Background

The population attributable fraction (PAF) is the fraction of disease cases in a sample that can be attributed to an exposure. Estimating the PAF often involves the estimation of the probability of having the disease given the exposure while adjusting for confounders. In many settings, the exposure can interact with confounders. Additionally, the exposure may have a monotone effect on the probability of having the disease, and this effect is not necessarily linear.

Methods

We develop a semiparametric approach for estimating the probability of having the disease and, consequently, for estimating the PAF, controlling for the interaction between the exposure and a confounder. We use a tensor product of univariate B-splines to model the interaction under the monotonic

SUBMITTER: Wang W 

PROVIDER: S-EPMC7507656 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature