has part has_specified_input has_specified_output count false positive rate between group comparison statistical test interim analysis Haybittle-Peto boundary analysis 10.1259/0007-1285-44-526-793 and adapted from Wikipedia: https://en.wikipedia.org/wiki/Haybittle%E2%80%93Peto_boundary Philippe Rocca-Serra The Haybittle–Peto boundary analysis is an interim analysis where a rule for deciding when to stop a clinical trial prematurely is defined. It is named for John Haybittle and Richard Peto. The Haybittle–Peto boundary is one such stopping rule, and it states that if an interim analysis shows a probability of equal to, or less than 0.001 that a difference as extreme or more between the treatments is found, given that the null hypothesis is true, then the trial should be stopped early. The final analysis is still evaluated at the normal level of significance (usually 0.05).[3][4] The main advantage of the Haybittle–Peto boundary is that the same threshold is used at every interim analysis, unlike the O'Brien–Fleming boundary, which changes at every analysis. Also, using the Haybittle–Peto boundary means that the final analysis is performed using a 0.05 level of significance as normal, which makes it easier for investigators and readers to understand. The main argument against the Haybittle–Peto boundary is that some investigators believe that the Haybittle–Peto boundary is too conservative and makes it too difficult to stop a trial. As all frequentist methods of the same type, it focuses on controlling the type I error rate as the repeated hypothesis testing of accumulating data increases the type I error rate of a clinical trial. p-value ready for release