Open AccessAnalysis of sparse data in logistic regression in medical research
Author(s) -
Devika Shanmugasundaram,
Lakshmanan Jeyaseelan,
G. Sebastian
Publication year - 2016
Publication title -
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 , confounding , confidence interval , odds ratio , hyponatremia , regression analysis , statistics , mathematics
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.
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