Author Archives: pmean

Recommended: Communicating Statistical Findings to Consulting Clients Operating in a Decisionmaking Climate: Best and Worst Practices

There were a large number of excellent talks at the 2014 Joint Statistics Meeting. This session discusses practical issues associated with communication. Although I did not attend this session, it looks pretty good and the speakers have all placed their slides in a single location. Continue reading →

Recommended: Sample size of 12 per group rule of thumb for a pilot study

This study is (sadly) not available for free on the Internet, but it is still worth highlighting here. Steven Julious provides some justification for the use of twelve patients per group in a pilot study. This is a useful starting point for discussion, and it may serve as a useful lower bound. I would suggest that you consider the size of the larger trial that you are piloting. For a larger study that might require thousands or tens of thousands of patients, a pilot study of 12 patients per group is woefully inadequate. Continue reading →

PMean: What is the probability of a probability of one

Someone wrote asking me about a variation of the “Rule of Three”. This rule says that if you observe zero events out of n, an upper 95% confidence limit for n is approximately 3/n. So suppose you operated on 10 patients and none of them died after surgery. Then you would be 95% confident that the mortality rate would be 30% (3/10) or less. This person asked “Suppose I repeatedly sample from a population and every patient in the sample was a G. What is the how likely is it that the entire population is Gs?” This flips the problem around, and is equivalent to saying that the probability of survival is 97% or greater. But this person wanted an estimate of the probability that the probability in the population is 1. Continue reading →

PMean: An example of a simple sample size justification

Someone asked me for a sample size justification for a study involving a historical control group of 30 patients and a treatment group of unspecified size. I thought it would be nice to document the mechanics of this calculation here, as an example for future clients. It uses a program, Piface, developed by Russ Lenth for sample size calculations. Continue reading →

Recommended: Special issue–Using Big Data to Transform Care

The July 2014 issue of Health Affairs is devoted entirely to “big data”.  The articles provide a general overview to big data, several applications of big data, big data and genomics, use of electronic health records, and ethical issues including privacy concerns. For now, at least, the articles are available for free to any user. Continue reading →

PMean: Simple longitudinal data sets to illustrate data management

I am working on a class that will teach basic data management and graphics using the R programming language with parallel classes in SPSS and SAS. On the third or fourth day of the class, we will look at managing longitudinal data sets, as these require special skills. I wanted to find a couple of reasonably simple longitudinal data sets that were available on the web and which had at least a few missing values in them. Here’s a couple of data sets that might work. Continue reading →