TY - JOUR
T1 - Statistical Identifiability and the Surrogate Endpoint Problem, with Application to Vaccine Trials
AU - Wolfson, Julian
AU - Gilbert, Peter
PY - 2010/12
Y1 - 2010/12
N2 - 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.
AB - 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.
KW - Estimated likelihood
KW - Identifiability
KW - Principal stratification
KW - Sensitivity analysis
KW - Surrogate endpoint
KW - Vaccine trials
UR - http://www.scopus.com/inward/record.url?scp=78650039193&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650039193&partnerID=8YFLogxK
U2 - 10.1111/j.1541-0420.2009.01380.x
DO - 10.1111/j.1541-0420.2009.01380.x
M3 - Article
C2 - 20105158
AN - SCOPUS:78650039193
SN - 0006-341X
VL - 66
SP - 1153
EP - 1161
JO - Biometrics
JF - Biometrics
IS - 4
ER -