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errorstatistics.com | ||
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philstatwars.wordpress.com
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| | | | | General Schedule PDF CLICK ON READINGS Topic: Current Controversies in Phil Stat (LSE, Remote 15:00 - 17:00; Thursdays 21 May-18 June) Main Text SIST:Statistical Inference as Severe Testing: How to Get Beyond the Statistics WarsCUP, 2018)*: Slides for each meeting will be on individual meeting posts on this blog. I. (May 21) Introduction: Controversies in... | |
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blog.apaonline.org
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| | | | | What is your work about? The "statistics wars" reflect disagreements on one of the deepest, oldest, philosophical questions: How do humans learn about the world despite threats of error due to incomplete and variable data? At the same time, the statistics wars are the engine behind current controversies surrounding high-profile failures of replication in the social | |
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fharrell.com
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| | | | | p-values are very often misinterpreted. p-values and null hypothesis significant testing have hurt science. This article attempts to catalog all the ways in which these happen. | |
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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. | ||
