Resources for COVID-19 Randomized Clinical Trial Design

Vanderbilt Institute for Clinical and Translational Science
Research Methods Program
Department of Biostatistics
Vanderbilt University School of Medicine
Nashville TN USA

• General COVID-19 Therapeutics Trial Design
• Web conference 2020-05-08 recording | Talks
• Bayesian fully sequential design and analysis plan for the 7-level COVID ordinal outcome scale for rapid learning and decision making
• 🆕 Ordinal Markov longitudinal modeling methods tutorial and case study using simulated VIOLET data
• Simulating longitudinal ordinal clinical trials and here
• Markov longitudinal ordinal model overview and discussion
• Longitudinal ordinal models as a general framework for medical outcomes
• Longitudinal ordinal analysis of ACTT-1 Remdesivir study and its analysis file creation and analysis by Max Rohde
• Longitudinal ordinal analysis of ORCHID and Restricted mean days in state by Max Rohde
• Longitudinal ordinal analysis of VIOLET and analysis of resimulated public VIOLET data
• Markov modeling for longitudinal data with irregular time points
• Discussion about the statistical model for the outcomes and related Bayesian modeling discussion
• Controlling α vs. Probability of a Decision Error
• Continuous learning from data: No multiplicities from computing and using Bayesian posterior probabilities as often as desired
• Simulating frequentist and Bayesian operating characteristics for a 3-level ordinal longitudinal outcome, also demonstrating how to translate data from a published trial into a simulation model for a new trial. An example shows that the usual time to recovery analysis effectively assumes proportional odds when deaths occur.
• Examples of Markov partial proportional odds models and comparison with time to recovery analysis in two simulated datasets
• Template and examples of simulating Bayesian operating characteristics
• Power Simulation Templates for Ordinal Longitudinal Outcomes
  • Comprehensive Markov model simulations (Bayesian and frequentist operating characteristics)
  • 3-level ordinal response with model derived from published data including Bayesian sequential analysis and pre-simulation of treatment effect standard errors
  • Bayesian sequential RCT with futility simulation
  • Efficiency as a function of frequency of longitudinal assessments
• Reporting Bayesian results by David Rindskopf
• Using Bayesian methods to augment the interpretation of critical care trials
• Violation of proportional odds is not fatal
• If you like the Wilcoxon test you must like the proportional odds model
• 🆕 Assessing the proportional odds assumption and its impact
• Discussion Board, which includes new updates regarding Stan code for the Bayesian PO model
• General rationale and methods for Bayesian clinical trial design and analysis and its overview
• Fundamental advantages of Bayes in drug development webinar
• R rms package Bayesian proportional odds model with random effects and implementing the partial proportional odds model to allow pre-specified departures from the proportional odds assumption (e.g. in a longitudinal ordinal outcome analysis the mix of outcomes may change over time but the treatment effect may be fairly constant)
• Nathan James’ R package for the Bayesian proportional odds model
• Stan code from Ben Goodrich (Columbia University) for the PO model and Ben’s notes
• Priors for ordinal regression by Michael Betancourt
• Inner workings of Stan ordinal models and Github
• Stan code repository from Ben Goodrich
• Discussion Board for COVID-19 research methods resources in general
• R code example of a frequentist mixed effects ordinal PO model vs. a Bayesian random effects ordinal model for longitudinal data
• Simulating ordinal outcome data by Keith Goldfeld
• Exploring the properties of a Bayesian model using high performance computing by Keith Goldfeld
• 🆕 References related to ordinal longitudinal and Markov models
• 🆕 References about ordinal regression
• Papers about statistical modeling of longitudinal ordinal responses
• Biostatistics in Biomedical Research Tutorials - see sections below:

• Tutorials on the proportional odds model and other ordinal models
• Risk prediction models for discrete ordinal outcomes: Calibration and the impact of the proportional odds assumption by M Edlinger, M van Smeeden, H Alber, M Wanitschek, B Van Calster
• Information gain from using ordinal instead of binary outcomes
• Bayesian vs. frequentist statements about treatment efficacy
• Journey from frequentist to Bayesian statistics
• A litany of problems with p-values
• P-values and type I errors are not the probabilities we need
• Null hypothesis significance testing never worked

Other Resources

• Paper exemplifying game playing in choice of RCT endpoint and how things go seriously wrong with time to symptom resolution as an endpoint (authors had to ignore emergency department visits)
• Detailed disease progression of 213 patients hospitalized with COVID-19 in the Czech Republic by Modrák et al
• Analysis of the Czech data
• Clinical Trial Tracker from Gates Foundation and Cytel
• Bayesian adaptive trial
• The P value line dance: When does the music stop? by Marcus Bendtsen
• Interim analysis for early stopping during the study by David Bock

Vanderbilt Department of Biostatistics