Simulating COVID-19 Trial with Bayesian Analysis
Explore a simulated COVID-19 trial focusing on pre-ICU patients receiving Hydroxychloroquine or Placebo, using Bayesian cumulative logistic regression to evaluate treatment effects based on the WHO COVID Ordinal Scale. Dive into the design skeleton, control rate assumptions, and the Bayesian cumulative logistic model for a comprehensive understanding of this trial setup.
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.
E N D
Presentation Transcript
Simulating a COVID-19 Trial An example of a possible trial
Design Skeleton Population: Pre-ICU COVID positive (or presumptive positive) patients Treatment arms: Hydroxychloroquine versus Placebo Fixed 1:1 randomization Endpoint: 8 point WHO COVID Ordinal Scale Primary Analysis: Bayesian cumulative logistic regression, assuming proportional odds effect Success criteria: Pr(OR>1) > 0.975, posterior probability of benefit is at least 97.5%
WHO COVID Ordinal Scale Control Rate Assumptions per level 1: Death 2% 2: IMV or ECMO 1% 3: NIV or HFNC 2% 4: Hospitalized w O2 7% 5: Hospitalized w/o O2 w/ in-patient needs 8% 6: Hospitalized w/o in- patient needs 10% 7: Not hospitalized w limitations 30% 8: Not hospitalized w/o limitations 40%
Bayesian cumulative logistic model ??? log = ??+ ? ??, ? = 1,2, ,7 1 ??? Where ???= Pr ?? ? and ??is a treatment indicator (1 = treated, 0 = control) ? is the log odds ratio, proportional across all levels of the outcome With priors: ??~ ???? 0,1 ? ~ ???? 0,1
