Statistical validation of the surrogate endpoints

A surrogate endpoint is intended to substitute for a clinical endpoint. A surrogate endpoint is expected to predict clinical benefit (or harm, or lack of benefit) based on epidemiologic, therapeutic, pathophysiologic or other scientific evidence. In clinical trials, a surrogate endpoint (or marker) is a measure of effect of a certain treatment that may correlate with a real endpoint but doesn't necessarily have a guaranteed relationship. The National Institutes of Health (USA) define surrogate endpoint as "a biomarker intended to substitute for a clinical endpoint"

Biomarkers are biological substances or features that can be used to indicate normal biological processes, disease processes, or responses to therapy. Biomarkers can be physiological indicators, such as heart rate or blood pressure, or they can be molecules in the tissues, blood, or other body fluids. For example, elevated blood levels of a protein called prostate specific antigen is a molecular biomarker for prostate cancer.

Biomarker and surrogate endpoint are often used interchangeably. However, there a subtle difference. Surrogate endpoints may not just be biomarkers and could include the imaging measurements (such as CT bone/lung densitometry, arteriogram...).

Just recently, I noticed that there are quite some works done in the area of statistical validadtion for surrogate endpoints. In the medical community, people may simply think that a biomarker can be a surrogate endpoint if the correlation between a surrogate endpoint and an established clinical endpoint are observed. However, the correlation is only one of the criteria (or requirement) for a biomarker to be a valid surrogate endpoint. To validate a surrogate endpoint, there have been a lot of discussions about the statistical approach in validating the surrogate endpoint.

in their paper titled "Surrogate end points in clinical trials: are we being misled?" (1996), Fleming and DeMets provided many examples of the surrogate endpoints and pointed out that these surrogate endpoints often fail in formal statistical validation.

The issues with surrogate endpoint is actually discussed in ICH E9 Statistical Principles for Clinical Trials

Surrogate Variables (2.2.6)
When direct assessment of the clinical benefit to the subject through observing
actual clinical efficacy is not practical, indirect criteria (surrogate variables — see
Glossary) may be considered. Commonly accepted surrogate variables are used in
a number of indications where they are believed to be reliable predictors of
clinical benefit. There are two principal concerns with the introduction of any
proposed surrogate variable. First, it may not be a true predictor of the clinical
outcome of interest. For example, it may measure treatment activity associated
with one specific pharmacological mechanism, but may not provide full information
on the range of actions and ultimate effects of the treatment, whether positive or
negative. There have been many instances where treatments showing a highly
positive effect on a proposed surrogate have ultimately been shown to be
detrimental to the subjects' clinical outcome; conversely, there are cases of
treatments conferring clinical benefit without measurable impact on proposed
surrogates. Second, proposed surrogate variables may not yield a quantitative
measure of clinical benefit that can be weighed directly against adverse effects.
Statistical criteria for validating surrogate variables have been proposed but the
experience with their use is relatively limited. In practice, the strength of the
evidence for surrogacy depends upon (i) the biological plausibility of the
relationship, (ii) the demonstration in epidemiological studies of the prognostic
value of the surrogate for the clinical outcome, and (iii) evidence from clinical
trials that treatment effects on the surrogate correspond to effects on the clinical
outcome. Relationships between clinical and surrogate variables for one product
do not necessarily apply to a product with a different mode of action for treating the
same disease.

Some key references:
1. Prentice, R. L. (1989). Surrogate endpoints in clinical trials: Definition and operational criteria. Statistics in Medicine 8 431–440
2. Freedman L, Graubard B (1992). Statistical validation of intermediate endpoints for chronic
diseases. Statistics in Medicine
3. Lin DY, Fleming TR, DeGruttola V. (1997) Estimating the proportion of treatment effect explained by a surrogate endpoint. Statistics in Medicine, 16:1515-1527
4. A framework for biomarker and surrogate endpoint in drug development by Janet Woodcock
5. Surrogate Markers - Their Role in Regulatory Decision Process
6. Statistical Validadtion of surrogate markers
7. Fleming and DeMets (1996) Surrogate End Points in Clinical Trials: Are We Being Misled? Ann Mem Med. 1996; 125:605-613