This page promotes a new approach to a broad class of models (spatio-temporal models, latent variable models, mixed models) using a fast approximation to the Bayesian solution. It runs under R and appears to handle very large datasets. I have not had a chance to try this, but it looks very interesting. Continue reading

# Monthly Archives: April 2016

# PMean: Calculating 90 day readmission rates

Someone asked me how to calculate a 90 day readmission rate from a large database. It’s a tricky problem because for many databases, it requires you to examine the data from a longitudinal perspective. Here’s some general advice. Continue reading

# Recommended: Interpretation of Changes in Health-related Quality of Life: The Remarkable Universality of Half a Standard Deviation

I’ve typically mocked the use of effect sizes in research, but perhaps I need to be a bit more open minded. This paper looked at the “minimally important difference” (note: not quite the same thing as the minimal clinically important difference) across 33 published studies of health related quality of life measures. Even though the structure of many of these measures was radically different, the minimally important difference was almost always close to 0.5. The authors draw an analogy to measurement on a seven point scale, where one unit is understood from previous psychological research to represent (roughly) the limit of human discrimination. Continue reading

# PMean: Some simple examples of single imputation

I wanted to use R to show some simple approaches to imputing missing values. These approaches are difficult to support because they require that you make some questionable and unverifiable assumptions about your data. They still may prove useful as a sensitivity check or as a springboard into more complex approaches for imputing missing values. I have a link to the code that generated most of these results. Continue reading

# PMean: Using version control through git, github, and R Studio

I’m definitely “old school” when it comes to programming, but there comes a time when even this old dog needs to learn a new trick. I decided yesterday to use version control for my own R programs. Nothing for clients, mind you, because of confidentiality concerns, but the R code that I use to develop teaching examples is certainly fair game. I’m not totally clueless on version control because of my work for the Greater Plains Collaborative, but it’s a different thing to do it totally by yourself. Here’s a brief outline of what I needed to do to get version control up and running. Continue reading

# PMean: Some open source Kaplan Meier curves

I’m giving a talk on the Kaplan-Meier survival curve and wanted to show and interpret a few real examples from the open source literature. Continue reading