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Examining evidence of time-dependent treatment effects: an illustration using regression methods

Examining evidence of time-dependent treatment effects: an illustration using regression methods

Authors :
Kim M. Jachno
Stephane Heritier
Robyn L. Woods
Suzanne Mahady
Andrew Chan
Andrew Tonkin
Anne Murray
John J. McNeil
Rory Wolfe
Source :
Trials, Vol 23, Iss 1, Pp 1-14 (2022)
Publication Year :
2022
Publisher :
BMC, 2022.

Abstract

Abstract Background For the design and analysis of clinical trials with time-to-event outcomes, the Cox proportional hazards model and the logrank test have been the cornerstone methods for many decades. Increasingly, the key assumption of proportionality—or time-fixed effects—that underpins these methods has been called into question. The availability of novel therapies with new mechanisms of action and clinical trials of longer duration mean that non-proportional hazards are now more frequently encountered. Methods We compared several regression-based methods to model time-dependent treatment effects. For illustration purposes, we used selected endpoints from a large, community-based clinical trial of low dose daily aspirin in older persons. Relative and absolute estimands were defined, and analyses were conducted in all participants. Additional exploratory analyses were undertaken by selected subgroups of interest using interaction terms in the regression models. Discussion In the trial with median 4.7 years follow-up, we found evidence for non-proportionality and a time-dependent treatment effect of aspirin on cancer mortality not previously reported in trial findings. We also found some evidence of time-dependence to an aspirin by age interaction for major adverse cardiovascular events. For other endpoints, time-fixed treatment effect estimates were confirmed as appropriate. Conclusions The consideration of treatment effects using both absolute and relative estimands enhanced clinical insights into potential dynamic treatment effects. We recommend these analytical approaches as an adjunct to primary analyses to fully explore findings from clinical trials.

Details

Language :
English
ISSN :
17456215
Volume :
23
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Trials
Publication Type :
Academic Journal
Accession number :
edsdoj.8d7d5d86455e4c91805ed11995d78f32
Document Type :
article
Full Text :
https://doi.org/10.1186/s13063-022-06803-x