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Result: Predicting analysis times in randomized clinical trials.

Title:
Predicting analysis times in randomized clinical trials.
Authors:
Bagiella E; Division of Biostatistics, Mailman School of Public Health, Columbia University, 622 W. 168th Street, New York, NY 10032, USA., Heitjan DF
Source:
Statistics in medicine [Stat Med] 2001 Jul 30; Vol. 20 (14), pp. 2055-63.
Publication Type:
Journal Article; Research Support, U.S. Gov't, P.H.S.
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Print ISSN: 0277-6715 (Print) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
Imprint Name(s):
Original Publication: Chichester ; New York : Wiley, c1982-
MeSH Terms:
Models, Biological* , Models, Statistical*, Randomized Controlled Trials as Topic/*methods, Bayes Theorem ; Computer Simulation ; Granulomatous Disease, Chronic/drug therapy ; Granulomatous Disease, Chronic/immunology ; Humans ; Immunotherapy ; Interferon-gamma/therapeutic use ; Randomized Controlled Trials as Topic/economics ; Randomized Controlled Trials as Topic/mortality ; Time Factors
Grant Information:
CA-13696 United States CA NCI NIH HHS; HL-53968 United States HL NHLBI NIH HHS
Substance Nomenclature:
82115-62-6 (Interferon-gamma)
Entry Date(s):
Date Created: 20010706 Date Completed: 20010823 Latest Revision: 20081121
Update Code:
20250114
DOI:
10.1002/sim.843
PMID:
11439420
Database:
MEDLINE

Further Information

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.
(Copyright 2001 John Wiley & Sons, Ltd.)