Inferential tools in penalized logistic regression for small and sparse data: A comparative study. (May 2018)

• Record Type: Journal Article
• Authors: Siino, Marianna; Fasola, Salvatore; Muggeo, Vito MR
• Is Part Of: Statistical methods in medical research. Volume 27:Number 5(2018)
• Page Start: 1365
• Page End: 1375
• Publication Date: 2018-05
• Subjects: Logistic regression -- Firth penalized likelihood -- sandwich formula -- score statistic -- gradient statistic; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280216661213
• Languages: English
• ISSNs: 0962-2802

Abstract

This paper focuses on inferential tools in the logistic regression model fitted by the Firth penalized likelihood. In this context, the Likelihood Ratio statistic is often reported to be the preferred choice as compared to the 'traditional' Wald statistic. In this work, we consider and discuss a wider range of test statistics, including the robust Wald, the Score, and the recently proposed Gradient statistic. We compare all these asymptotically equivalent statistics in terms of interval estimation and hypothesis testing via simulation experiments and analyses of two real datasets. We find out that the Likelihood Ratio statistic does not appear the best inferential device in the Firth penalized logistic regression.