Abstract
With logistic regressions, if sample sizes are small or strongly related to one of the binary outcomes, the estimated correlation coefficients may be biased. The same problem will, of course, occur, if your binary data are assessed in the form of contingency tables. This chapter is to assess Firth’s method as a possible solution for the purpose.
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Cleophas, T.J., Zwinderman, A.H. (2020). Firth’s Bias-adjusted Estimates for Biased Logistic Data Models (23 Challenger Launchings). In: Machine Learning in Medicine – A Complete Overview. Springer, Cham. https://doi.org/10.1007/978-3-030-33970-8_51
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DOI: https://doi.org/10.1007/978-3-030-33970-8_51
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Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33969-2
Online ISBN: 978-3-030-33970-8
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