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.

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Deep learning and other forms of machine learning are getting a lot of press in medicine. The reality doesn’t match the hype, and interpretable statistical models still have a lot to offer.


This article gives examples of information gained by using ordinal over binary response variables. This is done by showing that for the same sample size and power, smaller effects can be detected


I prefer fractions and ratios over percents. Here are the reasons.


It is easy to compute the sample size N1 needed to reliably estimate how one predictor relates to an outcome. It is next to impossible for a machine learning algorithm entertaining hundreds of features to yield reliable answers when the sample size < N1.


Methodologic goals and wishes for research and clinical practice for 2018




Recent & Upcoming Talks

The Next Generation of Clinical Trial Reporting
Nov 16, 2017 12:00 AM
Why Bayes for Clinical Trials?
Nov 16, 2017 12:00 AM


FDA Office of Biostatistics

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


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.