Statistical Identifiability and the Surrogate Endpoint Problem, with Application to Vaccine Trials
Article first published online: 22 JAN 2010
© 2010, The International Biometric Society
Volume 66, Issue 4, pages 1153–1161, December 2010
How to Cite
Wolfson, J. and Gilbert, P. (2010), Statistical Identifiability and the Surrogate Endpoint Problem, with Application to Vaccine Trials. Biometrics, 66: 1153–1161. doi: 10.1111/j.1541-0420.2009.01380.x
- Issue published online: 22 JAN 2010
- Article first published online: 22 JAN 2010
- Received December 2008. Revised November 2009. Accepted November 2009.
- Estimated likelihood;
- Principal stratification;
- Sensitivity analysis;
- Surrogate endpoint;
- Vaccine trials
Summary 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.