R Workflow for Reproducible Data Analysis and Reporting

This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of...

This is a place for discussions and Q&A about data-related issues and quantitative methods including study design, data analysis, and interpretation.

This is the story of what influenced me to become a Bayesian statistician after being trained as a classical frequentist statistician, and practicing only that mode of statistics for many years.

Machine learning approaches are increasingly suggested as tools to improve prediction of clinical outcomes. We aimed to identify when machine learning methods p...

Editorial Notes As Sander Greenland has so well stated below, this initial draft was still too “dichotomous” in that it was written assuming we would have different language for “positive” vs....

A prominent journal in cardiology has published one of the most flawed analyses I have seen in years. And the conclusions are even worse.

Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative...

A glossary of statistical terms, intended primarily for non-statisticians, may be found here. The purpose of this topic is to provide a place for suggested improvements to any of the existing...

R Workflow for Reproducible Data Analysis and Reporting

Prediction models are increasingly developed and used in diagnostic and prognostic studies, where the use of machine learning (ML) methods is becoming more and more popular over traditional regress...

Statistical Problems to Document and to Avoid Checklist for Authors References | Ten Simple Rules | Checklist to Avoid p-Hacking | Research Reliability and Publishing | Guidelines for Reporting of...

AbstractObjective. Methods to correct class imbalance (imbalance between the frequency of outcome events and nonevents) are receiving increasing interest f

When teaching we usually not only demonstrate/discuss how a certain method works, but, not less important, why it works. In contrast, the Wald confidence interval for a binomial p constitutes an ex...

This is the story of what influenced me to become a Bayesian statistician after being trained as a classical frequentist statistician, and practicing only that mode of statistics for many years.

Randomized clinical trials are successful because they do not mimic clinical practice. They remain highly clinically relevant despite this.

Guidance for biostatisticians on their essential contributions to clinical and translational research protocol review - Volume 5 Issue 1

A new almost-weekly web course Biostatistics for Biomedical Research will be launched on October 4, 2019. The course is intended both for consumers of research (understand study design, stat methods,...

I’m starting this with intent to have a good scrap/thread about how to interpret the ANDROMEDA-SHOCK trial: We’ve seen this basic problem before: most likely for budget/logistical reasons, the study...

I am conducting a regression analysis. I have a first problem and that is that the database has only an n of 41 (n=29 for one group and n=12 for the second group). But I have gone ahead with the lo...

As of May 27, 2020, there were over 1,678,843 confirmed cases of COVID-19 claiming more than 100,000 lives in the Unites States (CDC, 2020). Currently there is no known effective therapy or vaccine....

R Workflow for Reproducible Data Analysis and Reporting

This is post is to introduce members of the Cincinnati Children’s Hospital Medical Center R Users Group (CCHMC-RUG) to some of the functionality provided by Frank Harrell’s Hmisc and rms packages for...

The choice of drugs for patients with status epilepticus that is refractory to treatment with benzodiazepines has not been thoroughly studied. In a randomized, blinded, adaptive trial, we compared ...