RCT

The Burden of Demonstrating HTE

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.

Assessing Heterogeneity of Treatment Effect, Estimating Patient-Specific Efficacy, and Studying Variation in Odds ratios, Risk Ratios, and Risk Differences

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.

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.

Information Gain From Using Ordinal Instead of Binary Outcomes

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

Statistical Criticism is Easy; I Need to Remember That Real People are Involved

I have been critical of a number of articles, authors, and journals in this growing blog article. Linking the blog with Twitter is a way to expose the blog to more readers.

Continuous Learning from Data: No Multiplicities from Computing and Using Bayesian Posterior Probabilities as Often as Desired

(In a Bayesian analysis) It is entirely appropriate to collect data until a point has been proven or disproven, or until the data collector runs out of time, money, or patience.

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

Statistical Errors in the Medical Literature

Misinterpretation of P-values and Main Study Results Dichotomania Problems With Change Scores Improper Subgrouping Serial Data and Response Trajectories Cluster Analysis As Doug Altman famously wrote in his Scandal of Poor Medical Research in BMJ in 1994, the quality of how statistical principles and analysis methods are applied in medical research is quite poor.