drug-evaluation

RCT Analyses With Covariate Adjustment

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 on the GUSTO-I trial.

Bayesian Methods to Address Clinical Development Challenges for COVID-19 Drugs and Biologics

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.

Implications of Interactions in Treatment Comparisons

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.

Viewpoints on Heterogeneity of Treatment Effect and Precision Medicine

This article provides my reflections after the PCORI/PACE Evidence and the Individual Patient meeting on 2018-05-31. The discussion includes a high-level view of heterogeneity of treatment effect in optimizing treatment for individual patients.

Musings on Multiple Endpoints in RCTs

This article discusses issues related to alpha spending, effect sizes used in power calculations, multiple endpoints in RCTs, and endpoint labeling. Changes in endpoint priority is addressed. Included in the the discussion is how Bayesian probabilities more naturally allow one to answer multiple questions without all-too-arbitrary designations of endpoints as "primary" and "secondary". And we should not quit trying to learn.

Bayesian vs. Frequentist Statements About Treatment Efficacy

To avoid "false positives" do away with "positive". A good poker player plays the odds by thinking to herself "The probability I can win with this hand is 0.91" and not "

EHRs and RCTs: Outcome Prediction vs. Optimal Treatment Selection

Frank Harrell Professor of Biostatistics Vanderbilt University School of Medicine Laura Lazzeroni Professor of Psychiatry and, by courtesy, of Medicine (Cardiovascular Medicine) and of Biomedical Data Science Stanford University School of Medicine

My Journey From Frequentist to Bayesian Statistics

The difference between Bayesian and frequentist inference in a nutshell: With Bayes you start with a prior distribution for θ and given your data make an inference about the θ-driven process generating your data (whatever that process happened to be), to quantify evidence for every possible value of θ.

Randomized Clinical Trials Do Not Mimic Clinical Practice, Thank Goodness

What clinicians learn from clinical practice, unless they routinely do n-of-one studies, is based on comparisons of unlikes. Then they criticize like-vs-like comparisons from randomized trials for not being generalizable.