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Sample size adjustment designs with time-to-event outcomes: A caution
- Source :
- Clinical Trials. 14:597-604
- Publication Year :
- 2017
- Publisher :
- SAGE Publications, 2017.
-
Abstract
- Background: Sample size adjustment designs, which allow increasing the study sample size based on interim analysis of outcome data from a randomized clinical trial, have been increasingly promoted in the biostatistical literature. Although it is recognized that group sequential designs can be at least as efficient as sample size adjustment designs, many authors argue that a key advantage of these designs is their flexibility; interim sample size adjustment decisions can incorporate information and business interests external to the trial. Recently, Chen et al. (Clinical Trials 2015) considered sample size adjustment applications in the time-to-event setting using a design (CDL) that limits adjustments to situations where the interim results are promising. The authors demonstrated that while CDL provides little gain in unconditional power (versus fixed-sample-size designs), there is a considerable increase in conditional power for trials in which the sample size is adjusted. Methods: In time-to-event settings, sample size adjustment allows an increase in the number of events required for the final analysis. This can be achieved by either (a) following the original study population until the additional events are observed thus focusing on the tail of the survival curves or (b) enrolling a potentially large number of additional patients thus focusing on the early differences in survival curves. We use the CDL approach to investigate performance of sample size adjustment designs in time-to-event trials. Results: Through simulations, we demonstrate that when the magnitude of the true treatment effect changes over time, interim information on the shape of the survival curves can be used to enrich the final analysis with events from the time period with the strongest treatment effect. In particular, interested parties have the ability to make the end-of-trial treatment effect larger (on average) based on decisions using interim outcome data. Furthermore, in “clinical null” cases where there is no benefit due to crossing survival curves, the sample size adjustment design is shown to increase the probability of recommending an ineffective therapy. Conclusion: Access to interim information on the shape of the survival curves may jeopardize the perceived integrity of trials using sample size adjustment designs. Therefore, given the lack of efficiency advantage over group sequential designs, sample size adjustment designs in time-to-event settings remain unjustified.
- Subjects :
- Risk
Research design
Time Factors
Computer science
01 natural sciences
law.invention
010104 statistics & probability
03 medical and health sciences
0302 clinical medicine
Randomized controlled trial
law
Interim
Statistics
Econometrics
Humans
030212 general & internal medicine
0101 mathematics
Proportional Hazards Models
Randomized Controlled Trials as Topic
Event (probability theory)
Pharmacology
Flexibility (engineering)
General Medicine
Interim analysis
Clinical trial
Research Design
Sample size determination
Sample Size
Subjects
Details
- ISSN :
- 17407753 and 17407745
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- Clinical Trials
- Accession number :
- edsair.doi.dedup.....c39cb4034cd290658f441729ab208b75