TY - JOUR
T1 - Hard, harder, hardest
T2 - Principal stratification, statistical identifiability, and the inherent difficulty of finding surrogate endpoints
AU - Wolfson, Julian
AU - Henn, Lisa
N1 - Publisher Copyright:
© 2014Wolfson and Henn; licensee BioMed Central Ltd.
PY - 2014/8/26
Y1 - 2014/8/26
N2 - In many areas of clinical investigation there is great interest in identifying and validating surrogate endpoints, biomarkers that can be measured a relatively short time after a treatment has been administered and that can reliably predict the effect of treatment on the clinical outcome of interest. However, despite dramatic advances in the ability to measure biomarkers, the recent history of clinical research is littered with failed surrogates. In this paper, we present a statistical perspective on why identifying surrogate endpoints is so difficult. We view the problem from the framework of causal inference, with a particular focus on the technique of principal stratification (PS), an approach which is appealing because the resulting estimands are not biased by unmeasured confounding. In many settings, PS estimands are not statistically identifiable and their degree of non-identifiability can be thought of as representing the statistical difficulty of assessing the surrogate value of a biomarker. In this work, we examine the identifiability issue and present key simplifying assumptions and enhanced study designs that enable the partial or full identification of PS estimands. We also present example situations where these assumptions and designs may or may not be feasible, providing insight into the problem characteristics which make the statistical evaluation of surrogate endpoints so challenging.
AB - In many areas of clinical investigation there is great interest in identifying and validating surrogate endpoints, biomarkers that can be measured a relatively short time after a treatment has been administered and that can reliably predict the effect of treatment on the clinical outcome of interest. However, despite dramatic advances in the ability to measure biomarkers, the recent history of clinical research is littered with failed surrogates. In this paper, we present a statistical perspective on why identifying surrogate endpoints is so difficult. We view the problem from the framework of causal inference, with a particular focus on the technique of principal stratification (PS), an approach which is appealing because the resulting estimands are not biased by unmeasured confounding. In many settings, PS estimands are not statistically identifiable and their degree of non-identifiability can be thought of as representing the statistical difficulty of assessing the surrogate value of a biomarker. In this work, we examine the identifiability issue and present key simplifying assumptions and enhanced study designs that enable the partial or full identification of PS estimands. We also present example situations where these assumptions and designs may or may not be feasible, providing insight into the problem characteristics which make the statistical evaluation of surrogate endpoints so challenging.
KW - Causal inference
KW - Principal stratification
KW - Statistical identifiability
KW - Surrogate endpoint
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U2 - 10.1186/1742-7622-11-14
DO - 10.1186/1742-7622-11-14
M3 - Review article
C2 - 25342953
AN - SCOPUS:84907578684
SN - 1742-7622
VL - 11
JO - Emerging Themes in Epidemiology
JF - Emerging Themes in Epidemiology
IS - 1
M1 - 14
ER -