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Permanent link (DOI): https://doi.org/10.7939/R3901ZT68

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Multiple Outcomes in Heart Failure Research: Composite Endpoints and Multivariate Modelling Open Access

Descriptions

Other title
Subject/Keyword
multivariate
composite endpoint
heart failure
random effects
probability index
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Brown, Paul M
Supervisor and department
Justin A. Ezekowitz (Medicine)
Examining committee member and department
Padma Kaul (Public Health)
Finlay McAlister (Medicine)
Dean Eurich (Public Health)
Derek Exner (Medicine)
Department
Department of Medicine
Specialization

Date accepted
2017-06-07T15:38:01Z
Graduation date
2017-11:Fall 2017
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Composite endpoints are increasingly popular outcomes in clinical trials of heart failure. Uptake has outpaced guidance on their use and little consistency is seen in their construction. We must consider how best to handle multiple outcomes statistically and clinically, ie in a way that is both cogent for the clinical audience and statistically powerful. The clinical interpretation of composites has been emphasised along with its straightforward analysis and presentation. However there is a loss of information and a more thorough statistical analysis may offer advantages that are not easily dismissed, most obviously a gain in statistical efficiency and power. The modelling approach offers a number of other advantages: 1) adjustment for covariates, 2) a simple test of heterogeneity as the interaction between treatment and outcome, 3) analyses of the individual component endpoints are a consequence of the model, 4) correlations among outcomes are acknowledged, 5) recognises a constellation of risk factors or manifestations of the syndrome without blending them, 6) clinical weights are easily incorporated, and 7) an overall estimate of the effect is obtainable - making it comparable with the results from a composite endpoint. Thus the multivariate modelling approach yields a more powerful and thorough analysis without the loss of information that occurs when multiple outcomes are reduced to a single univariate composite measure. We use data simulations and real clinical trial data to illustrate and evaluate clinical composite endpoints and multivariate modelling. We developed SAS macros for data simulations and analysis methods which we make available.
Language
English
DOI
doi:10.7939/R3901ZT68
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
P. M. Brown, K. J. Anstrom, G. M. Felker, and J. A. Ezekowitz, “Composite end points in acute heart failure research: Data simulations illustrate the limitations,” Can J Cardiol, vol. 32, no. 11, pp. 1356.e21-1356.e28, 2016.P. M. Brown and J. A. Ezekowitz, “Composite end points in clinical trials of heart failure therapy: How do we measure the effect size?” Circ Heart Fail, vol. 10, no. 1, pii: e003222, 2017.

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