Premium
Exploring the relationship between the causal‐inference and meta‐analytic paradigms for the evaluation of surrogate endpoints
Author(s) -
Van der Elst Wim,
Molenberghs Geert,
Alonso Ariel
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6807
Subject(s) - inference , causal inference , surrogate endpoint , computer science , surrogate model , statistical inference , econometrics , machine learning , artificial intelligence , statistics , mathematics , medicine , radiology
Nowadays, two main frameworks for the evaluation of surrogate endpoints, based on causal‐inference and meta‐analysis, dominate the scene. Earlier work showed that the metrics of surrogacy introduced in both paradigms are related, although in a complex way that is difficult to study analytically. In the present work, this relationship is further examined using simulations and the analysis of a case study. The results indicate that the extent to which both paradigms lead to similar conclusions regarding the validity of the surrogate, depends on a complex interplay between multiple factors like the ratio of the between and within trial variability and the unidentifiable correlations between the potential outcomes. All the analyses were carried out using the newly developed R package Surrogate , which is freely available via CRAN. Copyright © 2015 John Wiley & Sons, Ltd.