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aurimas.eu | ||
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hbiostat.org
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| | | | | Continuous learning from data and computation of probabilities that are directly applicable to decision making in the face of uncertainty are hallmarks of the Bayesian approach. Bayesian sequential designs are the simplest of flexible designs, and continuous learning capitalizes on their efficiency, resulting in lower expected sample sizes until sufficient evidence is accrued due to the ability to take unlimited data looks. Classical null hypothesis testing only provides evidence against the supposition that a treatment has exactly zero effect, and it requires one to deal with complexities if not doing the analysis at a single fixed time. Bayesian posterior probabilities, on the other hand, can be computed at any point in the trial and provide current evidence about all possible questions, such as benefit, clinically relevant benefit, harm, and similarity of treatments. Besides requiring flexibility in a rapidly changing environment, COVID-19 trials often use ordinal endpoints and standard statistical models such as the proportional odds (PO) model. Less standard is how to model serial ordinal responses. Methods and new Baysian software have been developed for COVID-19 trials. Also implemented is a Bayesian partial PO model (Peterson and Harrell, 1990) that allows one to put a prior on the degree to which a treatment affects mortality differently than how it affects other components of the ordinal scale. These ordinal models will be briefly discussed. | |
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doomlab.github.io
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| | | | | Shiny Apps Each Shiny application is listed below, along with any relevant publication information. MOTE: Magnitude of the Effect Shiny App GitHub Research Paper This app allows you to calculate many effect sizes and their confidence intervals. We have included mean differences and variance overlap effect sizes, their formulas, easy to copy output in APA style, help videos on how to use our app, and code for R users. Alternatives to Null Hypothesis Significance Testing Shiny App GitHub Research Paper This app provides color visualization of the data from our paper that focuses on alternatives to NHST procedures. | |
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rpsychologist.com
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fharrell.com
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