an intervention’s benefit relative to its risk: sensitivity, measurability/interpretability, and clinical relevance. He illustrated the first criterion, sensitivity, with the choice of an endpoint for the trial of an analgesic for pain in preterminal cancer patients. Survival is critically important to such patients, he noted, but pain relief is the most sensitive measure of efficacy for this intervention.

The second criterion involves both measurability and interpretability. Dr. Fleming illustrated poor measurability with a hypothetical study requiring monthly liver biopsies. If such a study were conducted, it would not be acceptable to many patients and clinicians, and “you’re not going to retain patients very long.” He noted that interpretability is also important in selecting endpoints in clinical trials, and he provided some thoughts on composites of disease. One composite of disease, the combination of cardiovascular death, stroke, and myocardial infarction (MI), is interpretable because each of these conditions results in irreversible morbidity and mortality. However, interpretation becomes much more complicated if putative surrogate elements, such as “asymptomatic distal deep vein thrombosis,” are added to the composite (as they often are in studies of knee or hip replacement, he noted).

Clinical relevance of the endpoint is the ultimate criterion for its acceptance, according to Dr. Fleming. He referenced Robert Temple’s definition of a clinical endpoint: “a direct measure of how a patient functions, feels, and survives,” which is also reflected in the Biomarkers Definitions Working Group and the Institute of Medicine (IOM) committee’s reports (Biomarkers Definitions Working Group, 2001; IOM, 2010). Each of these attributes are difficult to measure, he acknowledged. Survival takes a long time to assess in many settings, and patient feelings and function are often based on patient-reported outcomes (PROs), which “can be very difficult to validate, often can have missing data, require blinding, and have a multiplicity associated with them,” he said. “It’s very tempting to look at objective alternative biomarkers.”

A common approach to finding such biomarkers is to identify one that is correlated with the desired clinical endpoint, show an effect in the biomarker, and make the “leap of faith” that this biomarker does, in fact, translate to clinical benefit, Dr. Fleming said. “Unfortunately,” he added, “that’s often not the case.” He proceeded to discuss the various reasons why a biomarker might fail as a surrogate endpoint, each of which are illustrated diagrammatically in Figure 7-1.

The first reason for a biomarker to fail as a surrogate endpoint is that the biomarker does not lie in the causal pathway by which the disease influences the clinical endpoint, so an intervention’s effect on the biomarker will not provide a reliable estimate of the intervention’s clinical efficacy (as shown in Figure 7-1A), Dr. Fleming said. An example of this

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