Predicting analysis times in randomized clinical trials.
Randomized clinical trial designs commonly include one or more planned interim analyses. At these times an external monitoring committee reviews the accumulated data and determines whether it is scientifically and ethically appropriate for the study to continue. With failure-time endpoints, it is common to schedule analyses at the times of occurrence of specified landmark events, such as the 50th event, the 100th event, and so on. Because interim analyses can impose considerable logistical burdens, it is worthwhile predicting their timing as accurately as possible. We describe two model-based methods for making such predictions during the course of a trial. First, we obtain a point prediction by extrapolating the cumulative mortality into the future and selecting the date when the expected number of deaths is equal to the landmark number. Second, we use a Bayesian simulation scheme to generate a predictive distribution of milestone times; prediction intervals are quantiles of this distribution. We illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease.
Bagiella
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Division of Biostatistics, Mailman School of Public Health, Columbia University, 622 W. 168th Street, New York, NY 10032, USA.
Heitjan
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D001499:Bayes Theorem; D003198:Computer Simulation; D006105:Granulomatous Disease, Chronic / Q000188:drug therapy / Q000276:immunology; D006801:Humans; D007167:Immunotherapy; D007371:Interferon-gamma / Q000627:therapeutic use; D008954:Models, Biological; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000191:economics / Q000379:methods* / Q000401:mortality; D013997:Time Factors
D007371:Interferon-gamma