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An optimal three‐stage design for phase II clinical trials
Author(s) -
Ensign Lisa Garnsey,
Gehan Edmund A.,
Kamen Douglas S.,
Thall Peter F.
Publication year - 1994
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780131704
Subject(s) - sample size determination , optimal design , statistical power , stage (stratigraphy) , statistics , sequential analysis , constraint (computer aided design) , computer science , design of experiments , mathematics , mathematical optimization , paleontology , geometry , biology
A phase II clinical trial in cancer therapeutics is usually a single‐arm study to determine whether an experimental treatment ( E ) holds sufficient promise to warrant further testing. When the criterion of treatment efficacy is a binary endpoint (response/no response) with probability of response p , we propose a three‐stage optimal design for testing H 0 : p ≤ p 0 versus H 1 : p ≥ p 1 , where p 1 and p 0 are response rates such that E does or does not merit further testing at given levels of statistical significance (α) and power (1 − β). The proposed design is essentially a combination of earlier proposals by Gehan and Simon. The design stops with rejection of H 1 at stage 1 when there is an initial moderately long run of consecutive treatment failures; otherwise there is continuation to stage 2 and (possibly) stage 3 which have decision rules analogous to those in stages 1 and 2 of Simon's design. Thus, rejection of H 1 is possible at any stage, but acceptance only at the final stage. The design is optimal in the sense that expected sample size is minimized when p = p 0 , subject to the practical constraint that the minimum stage 1 sample size is at least 5. The proposed design has greatest utility when the true response rate of E is small, it is desirable to stop early if there is a moderately long run of early treatment failures, and it is practical to implement a three‐stage design. Compared to Simon's optimal two‐stage design, the optimal three‐stage design has the following features: stage 1 is the same size or smaller and has the possibility of stopping earlier when 0 successes are observed; the expected sample size under the null hypothesis is smaller; stages 1 and 2 generally have more patients than stage 1 of the two‐stage design, but a higher probability of early termination under H 0 ; and the total sample size and criteria for rejection of H 1 at stage 3 are similar to the corresponding values at the end of stage 2 in the two‐stage optimal design.