Category Archives: Statistics

Pmean: The IRB questions my sample size calculation

I got a question today from someone submitting a research protocol to the Institutional Review Board (IRB). The IRB had some concerns about the power calculation. In particular, they said “The IRB would like to know, how you set the parameters for the power calculation, such as effect size, alpha level. For effect size, you need to have some data to justify or should choose a conservative one.”

Part of this was due to an error in the submission of the protocol. We had specified a paired t-test rather than an independent samples t-test, which is a major gaffe on my part. But they were pushing into some tricky territory and I wanted to clear things up. Here is the response that I suggested that we share with the IRB. Continue reading →

PMean: Using statistical design principles to plan a Monte Carlo analysis – part 2

I’ve been working more on a Monte Carlo study of various Bayesian estimators and it makes me think about certain principles that we statisticians use in experimental design that could help us not just with other people’s laboratory studies, but with Monte Carlo studies, which are our own laboratories. This is a continuation of an earlier blog post. One important principle is variable transformation. We almost always conceptualize and analyze proportions using the logit transformation, and this transformation can help a lot with Monte Carlo studies as well. Continue reading →

Recommended: Predicting clinical trial results based on announcements of interim analyses

If you’ve ever been involved with interim reviews of clinical trials on a DSMB (Data Safety and Monitoring Board), you will be warned about the importance of confidentiality. There are two big reasons for this. First, leaking of interim trial results could lead to insider trading. News that the trial is going well would lead to a jump in stock prices and news that the trial is going poorly would lead to a dip in stock prices. If someone gets early news from the DSMB, they could profit from that inside information. Continue reading →

PMean: Using statistical design principles to plan a Monte Carlo analysis

I want to run a Monte Carlo analysis of various Bayesian estimators to see how they perform when the prior distribution is “wrong”. I’m like everyone else–I just plunge in and start. But halfway through the Monte Carlo analysis, I realized that I could make my life easier and produce a better quality Monte Carlo analysis if I used basic statistical design principles. Here’s a brief outline of some of these design principles. Continue reading →