A Multinomial Regression Approach to Model Outcome Heterogeneity.

ABSTRACT: When a risk factor affects certain categories of a multinomial outcome but not others, outcome heterogeneity is said to be present. A standard epidemiologic approach for modeling risk factors of a categorical outcome typically entails fitting a polytomous logistic regression via maximum likelihood estimation. In this paper, we show that standard polytomous regression is ill equipped to detect outcome heterogeneity and will generally understate the degree to which such heterogeneity may be present. Specifically, nonsaturated polytomous regression will often a priori rule out the possibility of outcome heterogeneity from its parameter space. As a remedy, we propose to model each category of the outcome as a separate binary regression. For full efficiency, we propose to estimate the collectio

SUBMITTER: Sun B 

PROVIDER: S-EPMC5860474 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature