A new modeling and inference approach for the Systolic Blood Pressure Intervention Trial outcomes
Ambrosius, Walter T
Fine, Lawrence J
Bress, Adam P
Cushman, William C
Raj, Dominic S
AffiliationUniv Arizona, Coll Med
KeywordsAdaptively weighted log-rank test
average hazard ratio
MetadataShow full item record
PublisherSAGE PUBLICATIONS LTD
CitationYang, S., Ambrosius, W. T., Fine, L. J., Bress, A. P., Cushman, W. C., Raj, D. S., ... & Tamariz, L. (2018). A new modeling and inference approach for the Systolic Blood Pressure Intervention Trial outcomes. Clinical Trials, 15(3), 305-312. https://doi.org/10.1177/1740774518769865
RightsCopyright © 2018, © SAGE Publications
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractBackground/aims In clinical trials with time-to-event outcomes, usually the significance tests and confidence intervals are based on a proportional hazards model. Thus, the temporal pattern of the treatment effect is not directly considered. This could be problematic if the proportional hazards assumption is violated, as such violation could impact both interim and final estimates of the treatment effect. Methods We describe the application of inference procedures developed recently in the literature for time-to-event outcomes when the treatment effect may or may not be time-dependent. The inference procedures are based on a new model which contains the proportional hazards model as a sub-model. The temporal pattern of the treatment effect can then be expressed and displayed. The average hazard ratio is used as the summary measure of the treatment effect. The test of the null hypothesis uses adaptive weights that often lead to improvement in power over the log-rank test. Results Without needing to assume proportional hazards, the new approach yields results consistent with previously published findings in the Systolic Blood Pressure Intervention Trial. It provides a visual display of the time course of the treatment effect. At four of the five scheduled interim looks, the new approach yields smaller p values than the log-rank test. The average hazard ratio and its confidence interval indicates a treatment effect nearly a year earlier than a restricted mean survival time-based approach. Conclusion When the hazards are proportional between the comparison groups, the new methods yield results very close to the traditional approaches. When the proportional hazards assumption is violated, the new methods continue to be applicable and can potentially be more sensitive to departure from the null hypothesis.
VersionFinal accepted manuscript
SponsorsNational Institutes of Health [HHSN268200900040C]; CWRU [UL1TR000439]; OSU [UL1RR025755]; U Penn [UL1RR024134, UL1TR000 003]; U Boston [UL1RR025771]; U Stanford [UL1TR000093]; U Tufts [UL1RR025752, UL1TR000073, UL1TR001064]; University of Illinois [UL1TR000050]; University of Pittsburgh [UL1TR 000005]; UT Southwestern [9U54TR00 0017-06]; University of Utah [UL1TR000105-05]; Vanderbilt University [UL1 TR000445]; George Washington University [UL1TR000075]; UC Davis [UL1 TR000002]; University of Florida [UL1 TR000064]; University of Michigan [UL1TR000433]; Tulane University COBRE Award NIGMS [P30GM103337]; Wake Forest University [UL1TR 001420]; National Heart, Lung, and Blood Institute [HHSN268200900040C]
- The Average Hazard Ratio - A Good Effect Measure for Time-to-event Endpoints when the Proportional Hazard Assumption is Violated?
- Authors: Rauch G, Brannath W, Brückner M, Kieser M
- Issue date: 2018 May
- Interim monitoring using the adaptively weighted log-rank test in clinical trials for survival outcomes.
- Authors: Yang S
- Issue date: 2019 Feb 20
- Sequential tests for non-proportional hazards data.
- Authors: Brückner M, Brannath W
- Issue date: 2017 Jul
- Comparison of the restricted mean survival time with the hazard ratio in superiority trials with a time-to-event end point.
- Authors: Huang B, Kuan PF
- Issue date: 2018 May
- Are non-constant rates and non-proportional treatment effects accounted for in the design and analysis of randomised controlled trials? A review of current practice.
- Authors: Jachno K, Heritier S, Wolfe R
- Issue date: 2019 May 16