Statistical Identifiability and the Surrogate Endpoint Problem, with Application to Vaccine Trials

Julian Wolfson, Peter Gilbert

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Given a randomized treatment Z, a clinical outcome Y, and a biomarker S measured some fixed time after Z is administered, we may be interested in addressing the surrogate endpoint problem by evaluating whether S can be used to reliably predict the effect of Z on Y. Several recent proposals for the statistical evaluation of surrogate value have been based on the framework of principal stratification. In this article, we consider two principal stratification estimands: joint risks and marginal risks. Joint risks measure causal associations (CAs) of treatment effects on S and Y, providing insight into the surrogate value of the biomarker, but are not statistically identifiable from vaccine trial data. Although marginal risks do not measure CAs of treatment effects, they nevertheless provide guidance for future research, and we describe a data collection scheme and assumptions under which the marginal risks are statistically identifiable. We show how different sets of assumptions affect the identifiability of these estimands; in particular, we depart from previous work by considering the consequences of relaxing the assumption of no individual treatment effects on Y before S is measured. Based on algebraic relationships between joint and marginal risks, we propose a sensitivity analysis approach for assessment of surrogate value, and show that in many cases the surrogate value of a biomarker may be hard to establish, even when the sample size is large.

Original languageEnglish (US)
Pages (from-to)1153-1161
Number of pages9
JournalBiometrics
Volume66
Issue number4
DOIs
StatePublished - Dec 1 2010

Fingerprint

Surrogate Endpoint
Vaccines
Vaccine
Identifiability
endpoints
Biomarkers
vaccines
Treatment Effects
Principal Stratification
biomarkers
Risk Measures
Guidance
Sensitivity Analysis
Sample Size
Sensitivity analysis
Joints
Predict
Evaluation

Keywords

  • Estimated likelihood
  • Identifiability
  • Principal stratification
  • Sensitivity analysis
  • Surrogate endpoint
  • Vaccine trials

Cite this

Statistical Identifiability and the Surrogate Endpoint Problem, with Application to Vaccine Trials. / Wolfson, Julian; Gilbert, Peter.

In: Biometrics, Vol. 66, No. 4, 01.12.2010, p. 1153-1161.

Research output: Contribution to journalArticle

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