Quantifying and estimating additive measures of interaction from case-control data
Abstract
In this paper we develop a general framework for quantifying how binary risk factors jointly influence a binary outcome. Our key result is an additive expansion of odds ratios as a sum of marginal effects and interaction terms of varying order. These odds ratio expansions are used for estimating the excess odds ratio, attributable proportion and synergy index for a case-control dataset by means of maximum likelihood from a logistic regression model. The confidence intervals associated with these estimates of joint effects and interaction of risk factors rely on the delta method. Our methodology is illustrated with a large Nordic meta dataset for multiple sclerosis. It combines four studies, with a total of 6265 cases and 8401 controls. It has three risk factors (smoking and two genetic factors) and a number of other confounding variables.
Cite
@article{arxiv.1707.00911,
title = {Quantifying and estimating additive measures of interaction from case-control data},
author = {Ola Hössjer and Lars Alfredsson and Anna Karin Hedström and Magnus Lekman and Ingrid Kockum and Tomas Olsson},
journal= {arXiv preprint arXiv:1707.00911},
year = {2017}
}
Comments
Published at http://dx.doi.org/10.15559/17-VMSTA77 in the Modern Stochastics: Theory and Applications (https://www.i-journals.org/vtxpp/VMSTA) by VTeX (http://www.vtex.lt/)