Statistical Thinking

This blog is devoted to statistical thinking and its impact on science and everyday life. Emphasis is given to maximizing the use of information, avoiding statistical pitfalls, describing problems caused by the frequentist approach to statistical inference, describing advantages of Bayesian and likelihood methods, and discussing intended and unintended differences between statistics and data science. I’ll also cover regression modeling strategies, clinical trials, drug evaluation, medical diagnosis, and decision making.

Recent Posts

More Posts

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.

CONTINUE READING

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.

CONTINUE READING

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.

CONTINUE READING

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.

CONTINUE READING

Researchers have used contorted, inefficient, and arbitrary analyses to demonstrated added value in biomarkers, genes, and new lab measurements. Traditional statistical measures have always been up to the task, and are more powerful and more flexible. It’s time to revisit them, and to add a few slight twists to make them more helpful.

CONTINUE READING

Publications

2018

Selected Talks

Bayes for Flexibility in Urgent Times
2020-07-01
Sequential Bayesian Designs for Rapid Learning in COVID-19 Clinical Trials
2020-06-09
Fundamental Advantages of Bayes in Drug Development
2020-04-27
R for Graphical Clinical Trial Reporting
2020-01-29
R for Clinical Trial Reporting
2019-09-13
Controversies in Predictive Modeling, Machine Learning, and Validation
2019-06-04
Casual Inference Podcast
2020-04-23
Why Bayes for Clinical Trials?
2019-09-20
Bayesian Thinking Podcast
2019-08-07
Musings on Statistical Models vs. Machine Learning in Health Research
2019-05-02
Exploratory Analysis of Clinical Safety Data to Detect Safety Signals
2006-06-08

Other Talks

Bayes for Flexibility in Urgent Times
2020-07-01
Sequential Bayesian Designs for Rapid Learning in COVID-19 Clinical Trials
2020-06-09
Fundamental Advantages of Bayes in Drug Development
2020-04-27
R for Graphical Clinical Trial Reporting
2020-01-29
R for Clinical Trial Reporting
2019-09-13
Controversies in Predictive Modeling, Machine Learning, and Validation
2019-06-04
Simple Bootstrap and Simulation Approaches to Quantifying Reliability of High-Dimensional Feature Selection
2018-07-31
Current Challenges and Opportunities in Clinical Prediction Modeling
2018-07-02
Casual Inference Podcast
2020-04-23
Why Bayes for Clinical Trials?
2019-09-20
Bayesian Thinking Podcast
2019-08-07
Using R, Rmarkdown, RStudio, knitr, plotly, and HTML for the Next Generation of Reproducible Statistical Reports
2017-11-16
Musings on Statistical Models vs. Machine Learning in Health Research
2019-05-02
Exploratory Analysis of Clinical Safety Data to Detect Safety Signals
2006-06-08

Projects

FDA Office of Biostatistics

Enhancing capabilities of CDER and its Office of Biostatistics in Bayesian clinical trial design and analysis

Teaching

This is a free almost-weekly web course in introductory and intermediate biostatistics. Details are on the course web page.

Regression Modeling Strategies

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.

Master of Science in Clinical Investigation Biostatistics II

I co-teach this course at Vanderbilt each February for postdoctoral medical and surgical fellows and junior faculty in the MSCI program.

Contact

Datamethods

datamethods.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.