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


OBJECTIVE
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.


CONCLUSIONS
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.
Pryseley
Assam
A
Hasselt University, Center for Statistics, Agoralaan 1, B3590 Diepenbeek, Belgium.
Tilahun
Abel
A
Alonso
Ariel
A
Molenberghs
Geert
G
D001530:Belgium; D015415:Biomarkers / Q000032:analysis; D006801:Humans; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*
D015415:Biomarkers