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Repeated confidence intervals for adaptive group sequential trials
Statistics in medicine, 2007-12, Vol.26 (30), p.5422-5433
Mehta, Cyrus R.
Bauer, Peter
Posch, Martin
Brannath, Werner
2007
Volltextzugriff (PDF)
Details
Autor(en) / Beteiligte
Mehta, Cyrus R.
Bauer, Peter
Posch, Martin
Brannath, Werner
Titel
Repeated confidence intervals for adaptive group sequential trials
Ist Teil von
Statistics in medicine, 2007-12, Vol.26 (30), p.5422-5433
Ort / Verlag
Chichester, UK: John Wiley & Sons, Ltd
Erscheinungsjahr
2007
Quelle
Wiley-Blackwell Full Collection
Beschreibungen/Notizen
This paper proposes a method for computing conservative confidence intervals for a group sequential test in which an adaptive design change is made one or more times over the course of the trial. The key idea, due to Müller and Schäfer (Biometrics 2001; 57:886–891), is that by preserving the null conditional rejection probability of the remainder of the trial at the time of each adaptive change, the overall type I error rate, taken unconditionally over all possible design modifications, is also preserved. We show how this principle may be extended to construct one‐sided confidence intervals by applying the idea to a sequence of dual tests derived from the repeated confidence intervals (RCIs) proposed by Jennison and Turnbull (J. Roy. Statist. Soc. B 1989; 51:301–361). These adaptive RCIs, such as their classical counterparts, have the advantage that they preserve the desired coverage probability even if the pre‐specified stopping rule is over‐ruled. The statistical methodology is explored by simulations and is illustrated by an application to a clinical trial of deep brain stimulation for Parkinson's disease. Copyright © 2007 John Wiley & Sons, Ltd.
Sprache
Englisch
Identifikatoren
ISSN: 0277-6715
eISSN: 1097-0258
DOI: 10.1002/sim.3062
Titel-ID: cdi_proquest_miscellaneous_69069857
Format
–
Schlagworte
Bias
,
Biometry - methods
,
clinical trial
,
Clinical Trials as Topic - methods
,
Clinical Trials as Topic - statistics & numerical data
,
conditional power
,
Confidence Intervals
,
Data Interpretation, Statistical
,
Deep Brain Stimulation - statistics & numerical data
,
estimation in flexible design
,
Humans
,
inflation of type I error rate
,
Parkinson Disease - epidemiology
,
Parkinson Disease - therapy
,
Research Design - statistics & numerical data
,
Sample Size
,
sample size re-estimation
,
Treatment Outcome
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