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On the relationship between the causal‐inference and meta‐analytic paradigms for the validation of surrogate endpoints
Author(s) -
Alonso Ariel,
Van der Elst Wim,
Molenberghs Geert,
Buyse Marc,
Burzykowski Tomasz
Publication year - 2015
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12245
Subject(s) - causal inference , surrogate endpoint , inference , meta analysis , econometrics , statistical inference , surrogate model , computer science , statistics , mathematics , medicine , machine learning , artificial intelligence
Summary The increasing cost of drug development has raised the demand for surrogate endpoints when evaluating new drugs in clinical trials. However, over the years, it has become clear that surrogate endpoints need to be statistically evaluated and deemed valid, before they can be used as substitutes of “true” endpoints in clinical studies. Nowadays, two paradigms, based on causal‐inference and meta‐analysis, dominate the scene. Nonetheless, although the literature emanating from these paradigms is wide, till now the relationship between them has largely been left unexplored. In the present work, we discuss the conceptual framework underlying both approaches and study the relationship between them using theoretical elements and the analysis of a real case study. Furthermore, we show that the meta‐analytic approach can be embedded within a causal‐inference framework on the one hand and that it can be heuristically justified why surrogate endpoints successfully evaluated using this approach will often be appealing from a causal‐inference perspective as well, on the other. A newly developed and user friendly R package Surrogate is provided to carry out the evaluation exercise.