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- Title
Efficient estimation of the attributable fraction when there are monotonicity constraints and interactions.
- Authors
Traskin, Mikhail; Wang, Wei; Ten Have, Thomas R.; Small, Dylan S.
- Abstract
The PAF for an exposure is the fraction of disease cases in a population that can be attributed to that exposure. One method of estimating the PAF involves estimating the probability of having the disease given the exposure and confounding variables. In many settings, the exposure will interact with the confounders and the confounders will interact with each other. Also, in many settings, the probability of having the disease is thought, based on subject matter knowledge, to be a monotone increasing function of the exposure and possibly of some of the confounders. We develop an efficient approach for estimating logistic regression models with interactions and monotonicity constraints, and apply this approach to estimating the population attributable fraction (PAF). Our approach produces substantially more accurate estimates of the PAF in some settings than the usual approach which uses logistic regression without monotonicity constraints.
- Subjects
MONOTONIC functions; PROBABILITY theory; LOGISTIC regression analysis; ESTIMATION theory; MATHEMATICAL variables; MATHEMATICAL models
- Publication
Biostatistics, 2013, Vol 14, Issue 1, p173
- ISSN
1465-4644
- Publication type
Article
- DOI
10.1093/biostatistics/kxs019