Open AccessAnalysis of sparse data in logistic regression in medical research: A newer approach
Author(s) -
Devika Shanmugasundaram,
Lakshmanan Jeyaseelan,
G. Sebastian
Publication year - 2016
Publication title -
journal of postgraduate medicine/journal of postgraduate medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.405
H-Index - 52
eISSN - 0972-2823
pISSN - 0022-3859
DOI - 10.4103/0022-3859.173193
Subject(s) - medicine , logistic regression
In the analysis of dichotomous type response variable, logistic regression is usually used. However, the performance of logistic regression in the presence of sparse data is questionable. In such a situation, a common problem is the presence of high odds ratios (ORs) with very wide 95% confidence interval (CI) (OR: >999.999, 95% CI: <0.001, >999.999). In this paper, we addressed this issue by using penalized logistic regression (PLR) method.