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This is a super, super Twitter thread that nicely illustrates the problem of using survival analysis to predict positive outcomes (e.g., time to recovery). He does a great job in the first few tweets of reminding you what the data structure of a time-to-event data set looks like, and then promptly illustrates the problem when applied to something like “time to recovery” from COVID.

I know you’re not in my class anymore, and I won’t keep sending messages forever, but I also am unable to just turn off my teacher brain :-).

Michael

<https://urldefense.proofpoint.com/v2/url?u=https-3A__twitter.com_adalthousephd_status_1263457867983499264-3Fs-3D11&d=DwIGaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=SMBHqLg4oomrg4aZmZWrYe9nMmI6yWQ34bFrdYjZIZE&m=RsRMJdq05FlceZadfL2Y8iOxAkuiFoHxlclDZFu-6O0&s=Dr9P1yCFQt3C9ujJJAqllQ60dNL5Qn1StesuZpSEM20&e= >


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The NEJM compassionate-use remdesivir data was always sort of a "passing curiosity while we await trial data" but it's disappointing that the analysis was done wrong.

But, it offers an opportunity for a teachable moment about "time-to-event" analysis of non-mortality outcomes.

5/21/20, 9:13 AM<https://urldefense.proofpoint.com/v2/url?u=https-3A__twitter.com_adalthousephd_status_1263457867983499264-3Fs-3D11&d=DwIGaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=SMBHqLg4oomrg4aZmZWrYe9nMmI6yWQ34bFrdYjZIZE&m=RsRMJdq05FlceZadfL2Y8iOxAkuiFoHxlclDZFu-6O0&s=Dr9P1yCFQt3C9ujJJAqllQ60dNL5Qn1StesuZpSEM20&e= >



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Michael Marsiske
Professor
Department of Clinical and Health Psychology
University of Florida
PO Box 100165
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Website: http://marsiskelab.phhp.ufl.edu
Twitter: @MMarsiske