Planning and evaluating clinical trials with composite time-to-first-event endpoints in a competing risk framework
Composite endpoints combine several events of interest within a single variable. These are often time-to-first-event data, which are analyzed via survival analysis techniques. To demonstrate the significance of an overall clinical benefit, it is sufficient to assess the test problem formulated for t...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2 April 2013
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| In: |
Statistics in medicine
Year: 2013, Volume: 32, Issue: 21, Pages: 3595-3608 |
| ISSN: | 1097-0258 |
| DOI: | 10.1002/sim.5798 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1002/sim.5798 Verlag, lizenzpflichtig, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.5798 
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| Author Notes: | G. Rauch and J. Beyersmann |
| Summary: | Composite endpoints combine several events of interest within a single variable. These are often time-to-first-event data, which are analyzed via survival analysis techniques. To demonstrate the significance of an overall clinical benefit, it is sufficient to assess the test problem formulated for the composite. However, the effect observed for the composite does not necessarily reflect the effects for the components. Therefore, it would be desirable that the sample size for clinical trials using composite endpoints provides enough power not only to detect a clinically relevant superiority for the composite but also to address the components in an adequate way. The single components of a composite endpoint assessed as time-to-first-event define competing risks. We consider multiple test problems based on the cause-specific hazards of competing events to address the problem of analyzing both a composite endpoint and its components. Thereby, we use sequentially rejective test procedures to reduce the power loss to a minimum. We show how to calculate the sample size for the given multiple test problem by using a simply applicable simulation tool in SAS. Our ideas are illustrated by two clinical study examples. Copyright © 2013 John Wiley & Sons, Ltd. |
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| Item Description: | Gesehen am 16.12.2021 |
| Physical Description: | Online Resource |
| ISSN: | 1097-0258 |
| DOI: | 10.1002/sim.5798 |