Category Archives: Statistics

PMean: Cases and cohorts and controls, oh my!

Some asked a question about a retrospective study where you have a control cohort matched to a case cohort so the cohorts are similar on important (potentially confounding) variables. I pointed out that the two consecutive words “case cohort” are ambiguous and tried to explain  how I define a retrospective cohort design versus a (retrospective) case-control design. Continue reading

PMean: How to run your first Bayesian analysis using jags software in R

Someone wanted to know how to run a Bayesian data analysis for a two group longitudinal study. There are several ways you can do this, but I had to confess I did not have an immediate answer. So I took some time to figure out how to do this using jags software inside of R. I’ve done a fair amount of stuff in jags, but not anything close to a longitudinal design. The general principle is to start with something easy and work your way slowly up to the final analysis. Continue reading

Why secondary data analysis takes a lot longer

Someone posted a question noting that most of the statistical consulting projects that they worked on finished in a reasonable time frame, a few were outliers. They took a lot longer and required a lot more effort by the statisticians. Were there any common features to these outliers they wondered. So they asked if anyone else had identified methodological features of projects that went overtime. I only had a subjective impression, but thought it was still worth sharing. Continue reading

PMean: About those “awful” election predictions

If you were on Mars for the past few days, you may not have noticed that Donald Trump has won the election. There has been a lot of commentary lately about how badly the predictions about the U.S. election have been and someone mentioned that even Nate Silver at the fivethirtyeight website had a predicted probability of a Clinton win at 71%. I wrote a brief comment that predicting an event with 71% probability does not mean that your prediction was “wrong” if the other event occurs. Continue reading