Problems due to small samples and sparse data in conditional logistic regression analysis.

By Sander Greenland, J. Schwartzbaum, W. Finkle. American Journal of Epidemiology, 2000

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Maximum Likelihood and Firth Logistic Regression of the Pedestrian Route Choice (2017, background): “Anyhow, all categories had their counts enough for logistic regression, that is, equal to or more than 5 (Greenland, Schwartzbaum, and Finkle 2000).”
Large-Scale Bayesian Logistic Regression for Text Categorization (2007, background): “Exact logistic regression suffers from many of the same problems (Greenland, Schwartzbaum, and Finkle 2000).”
Inflammatory and Coagulation Biomarkers and Mortality in Patients with HIV Infection (2008, unknown): “To investigate the possibility that these large ORs were due to sparse data [27], the number of covariates considered [28], or outliers, four sensitivity analyses were performed: (1) conditional logistic models that included only baseline covariates that were signiﬁcant at the 0.10 level in a…”