Year: 2016
Type: article
Abstract: Likelihood-based inference of odds ratios in logistic regression models is problematic for small samples.For example, maximum-likelihood estimators may be seriously biased or even non-existent due to separation.Firth proposed a penalized likelihood approach which avoids these problems.However, his approach is based on a prospective sampling design and its application to case-control data has not yet been fully justified.To address the shortcomings of standard likelihood-based inference, we describe: i) naive application of Firth logistic regression, which ignores the case-control sampling design, and ii) an extension of Firth's method to case-control data proposed by Zhang.We present a simulation study evaluating the empirical performance of the two approaches in small to moderate case-control samples.Our simulation results suggest that even though there is no formal justification for applying Firth logistic regression to case-control data, it performs as well as Zhang logistic regression which is justified for case-control data.
Source: Summit (Simon Fraser University)
Author: Wen Ji-ying
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2588174429