Views expressed here are neither those of Vanderbilt University nor the US FDA
PhD in Biostatistics, 1979
UNC Chapel Hill
BS in Mathematics, 1973
University of Alabama Birmingham
This article summarizes arguments for the claim that the primary analysis of treatment effect in a RCT should be with adjustment for baseline covariates. It reiterates some findings and statements from classic papers, with illustration in the GUSTO-I trial.
The COVID-19 pandemic has elevated the challenge for designing and executing clinical trials with vaccines and drug/device combinations within a substantially shortened time frame. Numerous challenges in designing COVID-19 trials include lack of prior data for candidate interventions / vaccines due to the novelty of the disease, evolving standard of care and sense of urgency to speed up development programmes. We propose sequential and adaptive Bayesian trial designs to help address the challenges inherent in COVID-19 trials. In the Bayesian framework, several methodologies can be implemented to address the complexity of the primary endpoint choice. Different options could be used for the primary analysis of the WHO Severity Scale, frequently used in COVID-19 trials. We propose the longitudinal proportional odds mixed effects model using the WHO Severity Scale ordinal scale. This enables efficient utilization of all clinical information to optimize sample sizes and maximize the rate of acquiring evidence about treatment effects and harms.
This article explains how the generalizability of randomized trial findings depends primarily on whether and how patient characteristics modify (interact with) the treatment effect. For an observational study this will be related to overlap in the propensity to receive treatment.
Reasons are given for why heterogeneity of treatment effect must be demonstrated, not assumed. An example is presented that shows that HTE must exceed a certain level before personalizing treatment results in better decisions than using the average treatment effect for everyone.
This article shows an example formally testing for heterogeneity of treatment effect in the GUSTO-I trial, shows how to use penalized estimation to obtain patient-specific efficacy, and studies variation across patients in three measures of treatment effect.
Enhancing capabilities of CDER and its Office of Biostatistics in Bayesian clinical trial design and analysis
This is a free almost-weekly web course in introductory and intermediate biostatistics. Details are on the course web page.
I teach the BIOS7330 Regression Modeling Strategies course in the Biostatistics Graduate Program at Vanderbilt University in the spring semester. The course web page is here. I teach a 4-day version of this course each May at Vanderbilt. For 2020 the course will be held by webconference. Registration information for the short course may be found here.
I co-teach this course at Vanderbilt each February for postdoctoral medical and surgical fellows and junior faculty in the MSCI program.
See hbiostat.org for more up to date information.
stackexchangedatamethods.org is a discussion site where data methodologists meet each other and subject matter experts including clinical trialists and clinical researchers. Its development is documented here. Datamethods is provided by the Department of Biostatistics, Vanderbilt University School of Medicine.
I have written some short articles on the site, listed below.