Abstract
In a 1004 patient random sample, the effects of age, cholesterol levels, smoking levels, education levels, and weight levels on infarct rating was tested, both traditionally, and with the help of machine learning.
Traditional efficacy analysis consisted of
discretization of continuous predictors,
crosstabs with chi-square statistics.
Machine learning efficacy analysis consisted of binary decision-tree methods.
The machine learning methods provided better sensitivity of testing, and were more informative.
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Cleophas, T.J., Zwinderman, A.H. (2019). Binary Decision-Trees for Efficacy Analysis. In: Efficacy Analysis in Clinical Trials an Update. Springer, Cham. https://doi.org/10.1007/978-3-030-19918-0_12
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DOI: https://doi.org/10.1007/978-3-030-19918-0_12
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Online ISBN: 978-3-030-19918-0
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