underpowered-trial

To determine if a negative clinical trial was underpowered, you can perform reverse power calculations and confidence interval analysis to find the minimum detectable effect size for the trial's sample size. This distinguishes genuine missed opportunities from failed hypotheses.

Reverse power calculation is a statistical analysis technique that calculates the minimum detectable effect size for a given clinical trial's sample size and alpha level. It helps assess the credibility of claims that a trial was underpowered due to insufficient sample size.

You can calculate the required sample size for a confirmatory clinical trial by estimating it based on the observed effect sizes and applying shrinkage. This analysis evaluates the feasibility of moving a drug from a failed Phase 2 trial to a larger Phase 3 study.

Yes, you can use statsmodels and pandas for post-hoc power analysis on clinical trial data. The analysis assesses the trial's power to detect the observed effect size while critically evaluating if confidence intervals include clinically meaningful differences or the null.

A key limitation of post-hoc power analysis on failed drug development trials is that it must be interpreted with caveats. Confidence interval analysis is required alongside it to critically evaluate whether the interval includes clinically meaningful differences rather than relying solely on post-hoc power.

You need confidence interval analysis for hypothesis testing in drug development when evaluating if a negative trial's results include clinically meaningful differences or the null. This analysis helps uncover hidden signals in prior trials to determine if a confirmatory trial is warranted.