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Canadian Journal of Cardiology

Composite End Points in Acute Heart Failure Research: Data Simulations Illustrate the Limitations

Published:March 02, 2016DOI:https://doi.org/10.1016/j.cjca.2016.02.067

      Abstract

      Composite end points are frequently used in clinical trials of investigational treatments for acute heart failure, eg, to boost statistical power and reduce the overall sample size. By incorporating multiple and varying types of clinical outcomes they provide a test for the overall efficacy of the treatment. Our objective is to compare the performance of popular composite end points in terms of statistical power and describe the uncertainty in these power estimates and issues concerning implementation. We consider several composites that incorporate outcomes of varying types (eg, time to event, categorical, and continuous). Data are simulated for 5 outcomes, and the composites are derived and compared. Power is evaluated graphically while varying the size of the treatment effects, thus describing the sensitivity of power to varying circumstances and eventualities such as opposing effects. The average z score offered the most power, although caution should be exercised when opposing effects are anticipated. Results emphasize the importance of an a priori assessment of power and scientific basis for construction, including the weighting of individual outcomes deduced from data simulations. The interpretation of a composite should be made alongside results from the individual components. The average z score offers the most power, but this should be considered in the research context and is not without its limitations.

      Résumé

      On fait souvent appel à des paramètres d’évaluation mixtes lors des études cliniques sur les traitements expérimentaux de l’insuffisance cardiaque aiguë, p. ex., pour renforcer la puissance statistique et réduire la taille de la population à l’étude. L’intégration de plusieurs types d’issues cliniques variées permet de vérifier l’efficacité globale du traitement. Notre objectif est de comparer la performance des paramètres d’évaluation les plus courants, en fonction de la puissance statistique et de décrire le degré d’incertitude inhérent aux estimations de puissance et ainsi que les résultats de leur utilisation. Nous avons examiné plusieurs paramètres d’évaluation mixtes intégrant des types variés d’issues (p. ex., délai jusqu’à la survenue d’un événement, variable catégorique, variable continue). Les données ont été soumises à des simulations pour cinq issues cliniques et les paramètres d’évaluation mixtes ont ensuite été calculés et comparés. La puissance des paramètres a été évaluée graphiquement en faisant varier l’ampleur des effets du traitement, ce qui a permis de déterminer la sensibilité en fonction de diverses circonstances et éventualités telle la survenue d’effets contradictoires. C’est le score z moyen qui offrait la meilleure puissance, mais il faut faire preuve de prudence lors de son utilisation si on anticipe des effets contradictoires. Les résultats obtenus ont confirmé l’importance de procéder à une évaluation préalable de la puissance des paramètres et d’employer une méthodologie scientifique, y compris de pondérer les paramètres individuellement à partir des résultats obtenus lors des simulations de données. L’interprétation d’un paramètre d’évaluation mixte doit s’effectuer en parallèle avec celle des résultats obtenus pour ses composantes individuelles. Enfin, en contexte de recherche, c’est le score z moyen qui offre la meilleure puissance, bien qu’il comporte quelques limites.
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