Information-theory based surrogate marker evaluation from several randomized clinical trials with continuous true and binary surrogate endpoints

Abstract

Background: Surrogate endpoints potentially reduce the duration and/or increase the amount of information available in a study, thereby diminishing patient burden and cost. They may also increase the effectiveness and reliability of research, through beneficial impact on noncompliance and missingness.

Purpose: In this article, we review the meta-analytic approach of Buyse et al. (2000) and its extension to mixed continuous and binary endpoints by Molenberghs Geys, and Buyse (2001).

Methods: An information-theoretic alternative, based on Alonso and Molenberghs (2007a) is proposed. The method is evaluated using simulations and application to data from an ophthalmologic trial, with lines of vision lost at 6 months as candidate surrogate endpoints for lines of vision lost at 12 months. The method is implemented as an R function.

Results: The information-theoretic approach is based on solid theory, easy to apply, and enjoys elegant properties. While the information-theoretic approach appears to be somewhat biased downwards, this is due to fact that it operates at explicitly observed outcomes, without the need for unobserved, latent scales. This is a desirable property.

Limitations: While easy-to-use and implement, the theoretical foundation of the information-theory approach is more mathematical. It produces some bias for small to moderate trial/center sizes, and hence is recommended primarily for sufficiently large trials.

Conclusions: Since the meta-analytic framework can be computationally extremely expensive, the information-theoretic approach of Alonso and Molenberghs (2007a) is a viable alternative. For the ophthalmologic case study, the conclusion is that the lines of vision lost at sixth month do have some, but not overwhelming promise as a surrogate endpoint.