Phase II design with sequential testing of hypotheses within each stage

Stavroula Poulopoulou, Dimitris Karlis, Constantin T. Yiannoutsos, Urania Dafni

Research output: Contribution to journalArticle

Abstract

The main goal of a Phase II clinical trial is to decide, whether a particular therapeutic regimen is effective enough to warrant further study. The hypothesis tested by Flemings Phase II design (Fleming, 1982) is H0: p ≤ p0 versus HA: p > p0, with level and α with a power β at p = pA, where p0 is chosen to represent the response probability achievable with standard treatment and pA is chosen such that the difference pA ? p0 represents a targeted improvement with the new treatment. This hypothesis creates a misinterpretation mainly among clinicians that rejection of the null hypothesis is tantamount to accepting the alternative, and vice versa. As mentioned by Storer (1992), this introduces ambiguity in the evaluation of type I and II errors and the choice of the appropriate decision at the end of the study. Instead of testing this hypothesis, an alternative class of designs is proposed in which two hypotheses are tested sequentially. The hypothesis H 01: p≤ p0 versus HA1: p > p0 is tested first. If this null hypothesis is rejected, the hypothesis H 02: p ≥ pA versus HA2: p < pA is tested next, in order to examine whether the therapy is effective enough to consider further testing in a Phase III study. For the derivation of the proposed design the exact binomial distribution is used to calculate the decision cut-points. The optimal design parameters are chosen, so as to minimize the average sample number (ASN) under specific upper bounds for error levels. The optimal values for the design were found using a simulated annealing method.

Original languageEnglish (US)
Pages (from-to)768-784
Number of pages17
JournalJournal of biopharmaceutical statistics
Volume24
Issue number4
DOIs
StatePublished - Jul 4 2014

Keywords

  • Clinical trials
  • Multistage
  • Phase II trials
  • Sequential testing

ASJC Scopus subject areas

  • Pharmacology (medical)
  • Pharmacology
  • Statistics and Probability
  • Medicine(all)

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