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A sequential procedure for monitoring clinical trials against historical controls
Author(s) -
Xiong Xiaoping,
Tan Ming,
Boyett James
Publication year - 2006
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.2635
Subject(s) - interim , interim analysis , population , computer science , randomization , clinical trial , sample size determination , type i and type ii errors , test (biology) , sequential analysis , statistic , test statistic , statistics , randomized controlled trial , statistical hypothesis testing , data monitoring committee , medicine , mathematics , surgery , paleontology , environmental health , archaeology , pathology , biology , history
In this paper, we develop a sequential procedure to monitor clinical trials against historical controls. When there is a strong ethical concern about randomizing patients to existing treatment because biological and medical evidence suggests that the new treatment is potentially superior to the existing one, or when the enrollment is too limited for randomization of subjects into experimental and control groups, one can monitor the trial sequentially against historical controls if the historical data with required quality and sample size are available to form a valid reference for the trial. This design of trial is sometimes the only alternative to a randomized phase III trial design that is intended but not feasible in situations such as above. Monitoring this type of clinical trial leads to a statistical problem of comparing two population means in a situation in which data from one population are sequentially collected and compared with all data from the other population at each interim look. The proposed sequential procedures is based on the sequential conditional probability ratio test (SCPRT) by which the conclusion of the sequential test would be virtually the same as that arrived at by a non‐sequential test based on all data at the planned end of the trial. We develop the sequential procedure by proposing a Brownian motion that emulates the test statistic, and then proposing an SCPRT that is adapted to the special properties of the trial. Copyright © 2006 John Wiley & Sons, Ltd.