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Interim analysis in clinical trials
Author(s) -
Armitage Peter
Publication year - 1991
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.4780100613
Subject(s) - interim , data monitoring committee , interim analysis , protocol (science) , bayesian probability , computer science , relevance (law) , delegate , clinical trial , operations research , medicine , artificial intelligence , mathematics , law , alternative medicine , pathology , political science , programming language
The early development of experimental design discouraged a sequential approach to the analysis of data, yet this seems a natural form of scientific enquiry. Clinical trial investigators should continuously monitor the quality of their techniques, but will often wish to delegate data monitoring to an independent group. The history and functions of data monitoring committees (DMCs) are reviewed. DMCs come in many shapes and sizes. They will need to consider many aspects of the data before making recommendations to the investigators, who have ultimate responsibility for early termination or protocol changes. Statistical issues form part of the assessment, and will involve management, safety and efficacy. Two broad approaches to early stopping are (i) the demonstration of strong evidence that a treatment effect falls above or below some critical value (not necessarily zero); (ii) stochastic curtailment based on prediction of final results. The latter is examined somewhat critically. Most trials will involve group sequential analyses at discrete time points. The effect of repeated data inspection on (i) is well known, although its relevance is debatable. Bayesian and likelihood methods do not entirely remove the difficulty.