Monthly Archives: June 2014

PMean: A biased sample of car speeds

Dear Professor Mean, I read a newspaper report about speed limits and how few people obeyed them. A reporter decided to collect some hard data and drove exactly at the speed limit (55 mph in this particular setting). The reporter noticed that nine cars passed his car for every car that he passed, and concluded that most people are breaking the speed limit. I’m wondering if this is really a valid way to collect data. Continue reading →

PMean: Sample size for a study of reproducibility

Dear Professor Mean: I am using a risk stratification tool for patients presenting to the ED with chest pain. This has been a well validated tool in the ED, but I want to show that the scores are reproducible irrespective of the grade of doctor or assessment nurse calculating the score. I’m going to collect a convenience sample of patients presenting to the ED, and after I get informed consent, I will have those patients assessed separately by a triage-trained nurse, an intern doctor, a registrar and a consultant. I will calculation agreement using the intraclass correlation coefficient (ICC). My question is: How do I calculate the sample size in this context? Continue reading →

Recommended: Comparisons within randomised groups can be very misleading

In studies with a baseline, examining the decline exclusively within the treated group, or examining the decline in the treated group and then separately examining the decline in the control group is a bad idea, notes two famous statisticians in the British Medical Journal. They explain why you need to look first at comparisons between the two groups, ideally with analysis of covariance. Continue reading →