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Assessment of the information theory approach to evaluating time-to-event surrogate and true endpoints in a meta-analytic setting

Dimier, N. and Todd, S. ORCID: https://orcid.org/0000-0002-9981-923X (2021) Assessment of the information theory approach to evaluating time-to-event surrogate and true endpoints in a meta-analytic setting. Pharmaceutical Statistics, 20 (2). pp. 335-347. ISSN 1539-1612

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To link to this item DOI: 10.1002/pst.2080

Abstract/Summary

In many disease areas, commonly used long‐term clinical endpoints are becoming increasingly difficult to implement due to long follow‐up times and/or increased costs. Shorter‐term surrogate endpoints are urgently needed to expedite drug development, the evaluation of which requires robust and reliable statistical methodology to drive meaningful clinical conclusions about the strength of relationship with the true long‐term endpoint. This paper uses a simulation study to explore one such previously proposed method, based on information theory, for evaluation of time to event surrogate and long‐term endpoints, including the first examination within a meta‐analytic setting of multiple clinical trials with such endpoints. The performance of the information theory method is examined for various scenarios including different dependence structures, surrogate endpoints, censoring mechanisms, treatment effects, trial and sample sizes, and for surrogate and true endpoints with a natural time‐ordering. Results allow us to conclude that, contrary to some findings in the literature, the approach provides estimates of surrogacy that may be substantially lower than the true relationship between surrogate and true endpoints, and rarely reach a level that would enable confidence in the strength of a given surrogate endpoint. As a result, care is needed in the assessment of time to event surrogate and true endpoints based only on this methodology.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics > Applied Statistics
ID Code:93711
Publisher:Wiley

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