Hard, harder, hardest: principal stratification, statistical identifiability, and the inherent difficulty of finding surrogate endpoints

Emerging Themes in Epidemiology

Lisa Henn¹  Julian Wolfson¹ 

[1] University of Minnesota Division of Biostatistics, A460 Mayo Building MMC 303, 420 Delaware St SE, Minneapolis MN, USA

关键词: Statistical identifiability;    Causal inference;    Principal stratification;    Surrogate endpoint;   

Others  :  1092982
DOI  :  10.1186/1742-7622-11-14

 received in 2014-05-19, accepted in 2014-08-14,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Wolfson and Henn; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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