Author Archives: pmean

PMean: Calculating power for a test of association in a two by two table

A colleague was curious to see the formulas behind the power calculations done by many statistical software programs and online calculators. In particular, she wanted to see the formula used for power of the Chi-squared test of association in a two dimensional contingency table. It gets pretty messy for anything larger than a two by two table, but even a two by two table is a bit tricky. Here ins one mathematical approach that you can choose for a power calculation. Continue reading →

PMean: A rant about the “reader pays” model of publication

Something came up in our department about a predatory pay for access journal that was soliciting support. All the appropriate warnings were made (there’s a nice explanation of predatory open access publishing at Wikipedia, if you’re curious). But I felt that I had to made a strong defense of the value provided by legitimate open access publishers. Here’s a summary of what I wrote. Continue reading →

PMean: How detailed should I make my data analysis plan.

Dear Professor Mean, I could use some advice on defining and following analysis plans for research proposals. I can see how in well-trodden research, where the nuances of the data are well understood, and reasonable distributional assumptions are already identified, a detailed analysis plan may be straightforward to develop. But what about cases where you collect observational data, and though you may have specific hypotheses in mind prior to collecting it, you can’t really pin down the most appropriate analysis until after you’ve done some exploration? Is there generally an acceptable amount of leeway in cases like these, where your analysis doesn’t follow the original plan to the dot, but it’s still designed to address the same question? Or must one be as specific and detailed in the plan as possible, and consider every contingency (e.g., if the distribution is noisy, switch to this non-parametric test)? Or does it really vary with whoever the reviewer is? Continue reading →

PMean: Information about the Great Plains Collaborative Breast Cancer Study

This page is moving to a new website.

I’m starting a new project as an independent consultant. Normally, I talk very little on my blog about specific projects that I work for, but this work is for a group, the Great Plains Collaborative (GPC) that is open about almost every aspect of the work they do. That’s music to my ears. Anyway, the GPC is involved with several projects and one of the ones I might do some of my work on is the Great Plains Collaborative Breast Cancer Study. Here is some information about this study, culled from sources available to anyone on the Internet. Continue reading →

Recommended: The Empirical Evidence of Bias in Trials Measuring Treatment Differences

When I wrote a book about Evidence Based Medicine back in 2006, I talked about empirical evidence to support the use of certain research methodologies like blinding and allocation concealment. Since that time, many more studies have appeared, more than you or I could easily keep track of. Thankfully, the folks at the Agency for Healthcare Research and Quality commissioned a report to look at studies that empirically evaluate the bias reduction of several popular approaches used in randomized trials. These include

selection bias through randomization (sequence generation and allocation concealment); confounding through design or analysis; performance bias through fidelity to the protocol, avoidance of unintended interventions, patient or caregiver blinding and clinician or provider blinding; detection bias through outcome assessor and data analyst blinding and appropriate statistical methods; detection/performance bias through double blinding; attrition bias through intention-to-treat analysis or other approaches to accounting for dropouts; and reporting bias through complete reporting of all prespecified outcomes.

The general finding was that failure to use these bias reduction approaches tended to exaggerate treatment effects, but the magnitude and precision of these exaggerated effects was inconsistent. Continue reading →