It’s often overlooked how the assumption of effect size can significantly influence sample size estimates in clinical trials. Early in my career, I used standard conventions without questioning their applicability to my specific context. I’m curious if anyone has utilized simulation methods to derive more tailored sample size calculations and what tools or approaches you found beneficial.
I’ve faced the same issue with effect size assumptions. Using simulation methods like R packages designed for power analysis really helped us tailor our sample sizes more effectively, especially when traditional methods felt off. Have you tried any specific simulation software yet?
I totally get where you’re coming from! In one trial, we used simulations to explore different effect sizes, and it felt like adjusting a recipe until everything just clicked. @liam_sullivan29, have you tried using specific software designed for these calculations, or do you prefer building your own models?
Great point about effect sizes! I once found that simulating various scenarios helped clarify what I really needed, almost like fine-tuning a musical instrument. @robert_w79, ever tried using tools like G*Power to visualize those adjustments?