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

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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.

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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.

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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.

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The performance metrics chosen for prediction tools, and for Machine Learning in particular, have significant implications for health care and a penetrating understanding of the AUROC will lead to better methods, greater ML value, and ultimately, benefit patients.

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This article lays out the rationale and overall design of a new discussion site about quantitative methods.

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Publications

2018

Selected Talks

R for Clinical Trial Reporting
2019-09-13
Controversies in Predictive Modeling, Machine Learning, and Validation
2019-06-04
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

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
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
Using R, Rmarkdown, RStudio, knitr, plotly, and HTML for the Next Generation of Reproducible Statistical Reports
2017-11-16
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. 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.