Abstract
BackgroundThere is common belief among some medical researchers that if a potential surrogate endpoint is highly correlated with a true endpoint, then a positive (or negative) difference in potential surrogate endpoints between randomization groups would imply a positive (or negative) difference in unobserved true endpoints between randomization groups. We investigate this belief when the potential surrogate and unobserved true endpoints are perfectly correlated within each randomization group.MethodsWe use a graphical approach. The vertical axis is the unobserved true endpoint and the horizontal axis is the potential surrogate endpoint. Perfect correlation within each randomization group implies that, for each randomization group, potential surrogate and true endpoints are related by a straight line. In this scenario the investigator does not know the slopes or intercepts. We consider a plausible example where the slope of the line is higher for the experimental group than for the control group.ResultsIn our example with unknown lines, a decrease in mean potential surrogate endpoints from control to experimental groups corresponds to an increase in mean true endpoint from control to experimental groups. Thus the potential surrogate endpoints give the wrong inference. Similar results hold for binary potential surrogate and true outcomes (although the notion of correlation does not apply). The potential surrogate endpointwould give the correct inference if either (i) the unknown lines for the two group coincided, which means that the distribution of true endpoint conditional on potential surrogate endpoint does not depend on treatment group, which is called the Prentice Criterion or (ii) if one could accurately predict the lines based on data from prior studies.ConclusionPerfect correlation between potential surrogate and unobserved true outcomes within randomized groups does not guarantee correct inference based on a potential surrogate endpoint. Even in early phase trials, investigators should not base conclusions on potential surrogate endpoints in which the only validation is high correlation with the true endpoint within a group.
Highlights
There is common belief among some medical researchers that if a potential surrogate endpoint is highly correlated with a true endpoint, a positive difference in potential surrogate endpoints between randomization groups would imply a positive difference in unobserved true endpoints between randomization groups
It has long been assumed that postmenopausal hormone replacement therapy (HRT) with estrogen and progestin would decrease the risk of cardiac disease in women-in part due to the facts that (1) hormonal therapy lowers serum cholesterol, and (2) people with low cholesterol generally have a lowered risk of cardiac disease
They added that arequirement for a valid surrogate is that" the effect of the intervention on the potential surrogate endpoint predicts the effect on clinical outcome---a much stronger condition than correlation." Using schema for causal pathways, Fleming and DeMets [1] showed why a potential surrogate endpoint can fail to provide correct inference about the true endpoint
Summary
There is common belief among some medical researchers that if a potential surrogate endpoint is highly correlated with a true endpoint, a positive (or negative) difference in potential surrogate endpoints between randomization groups would imply a positive (or negative) difference in unobserved true endpoints between randomization groups. Fleming and DeMets [1] and the Biomarker Definitions Working Group [2] gave various examples where preliminary inference based on a potential surrogate endpoint was contradicted by later studies using important health outcomes. As another example, it has long been assumed that postmenopausal hormone replacement therapy (HRT) with estrogen and progestin would decrease the risk of cardiac disease in women-in part due to the facts that (1) hormonal therapy lowers serum cholesterol, and (2) people with low cholesterol generally have a lowered risk of cardiac disease. The main purpose of this paper is to show graphically that even a perfect correlate within a randomized group does not a valid surrogate make
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