A maximum entropy approach for the evaluation of surrogate endpoints based on causal inference.

The maximum entropy principle offers a constructive criterion for setting up probability distributions on the basis of partial knowledge. In the present work, the principle is applied to tackle an important problem in the surrogate marker field, namely, the evaluation of a binary outcome as a putative surrogate for a binary true endpoint within a causal inference framework. In the first step, the maximum entropy principle is used to determine the relative frequencies associated with the values of the vector of potential outcomes. Subsequently, in the second step, these relative frequencies are used in combination with two newly proposed metrics of surrogacy, the so-called individual causal association and the surrogate predictive function, to assess the validity of the surrogate. The procedure is conceptually similar to the use of noninformative or reference priors in Bayesian statistics. Additionally, approximate, identifiable bounds are proposed for the estimands of interest, and their performance is studied via simulations. The methods are illustrated using data from a clinical trial involving schizophrenic patients, and a newly developed and user-friendly R package Surrogate is provided to carry out the validation exercise.