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hbiostat.org | ||
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fharrell.com
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| | | | | In this article I provide much more extensive simulations showing the near perfect agreement between the odds ratio (OR) from a proportional odds (PO) model, and the Wilcoxon two-sample test statistic. The agreement is studied by degree of violation of the PO assumption and by the sample size. A refinement in the conversion formula between the OR and the Wilcoxon statistic scaled to 0-1 (corcordance probability) is provided. | |
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fharrell.com
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| | | | | This article briefly discusses why the rank difference test is better than the Wilcoxon signed-rank test for paired data, then shows how to generalize the rank difference test using the proportional odds ordinal logistic semiparametric regression model. To make the regression model work for non-independent (paired) measurements, the robust cluster sandwich covariance estimator is used for the log odds ratio. Power and type I assertion \alpha probabilities are compared with the paired t-test for n=25. The ordinal model yields \alpha=0.05 under the null and has power that is virtually as good as the optimum paired t-test. For non-normal data the ordinal model power exceeds that of the parametric test. | |
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fharrell.com
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| | | | | This article provides my reflections after the PCORI/PACE Evidence and the Individual Patient meeting on 2018-05-31. The discussion includes a high-level view of heterogeneity of treatment effect in optimizing treatment for individual patients. | |
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sharpsight.ai
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| | | This tutorial is part 2 of our covid-19 data analysis using Python. For more data science tutorials, sign up for our email list. | ||
