Writing
Notes on statistical methods, research software, and evidence-based practice. Written for clinicians and researchers, not just statisticians.
The choice isn't philosophical. It depends on your prior information, regulatory context, and what you're trying to communicate to reviewers.
Most analysis code is write-once. Here's how to build workflows your team can actually maintain, audit, and extend without heroics.
An SAP that's too vague gets you in trouble with reviewers. One that's too rigid ties your hands mid-study. Threading that needle takes practice.
Most power calculations are optimistic by design. Here's how to pressure-test yours before the IRB does.
Adaptive designs can dramatically reduce sample size — or introduce bias that sinks your results. The difference is in the details.
Two tools, properly configured, eliminate almost every 'it works on my machine' problem in R-based research projects.
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