Evaluating surrogate endpoints, prognostic markers, and predictive markers: Some simple themes

Abstract

Background: A surrogate endpoint is an endpoint observed earlier than the true endpoint (a health outcome) that is used to draw conclusions about the effect of treatment on the unobserved true endpoint. A prognostic marker is a marker for predicting the risk of an event given a control treatment; it informs treatment decisions when there is information on anticipated benefits and harms of a new treatment applied to persons at high risk. A predictive marker is a marker for predicting the effect of treatment on outcome in a subgroup of patients or study participants; it provides more rigorous information for treatment selection than a prognostic marker when it is based on estimated treatment effects in a randomized trial.

Methods: We organized our discussion around a different theme for each topic.

Results: "Fundamentally an extrapolation" refers to the non-statistical considerations and assumptions needed when using surrogate endpoints to evaluate a new treatment. "Decision analysis to the rescue" refers to use the use of decision analysis to evaluate an additional prognostic marker because it is not possible to choose between purely statistical measures of marker performance. "The appeal of simplicity" refers to a straightforward and efficient use of a single randomized trial to evaluate overall treatment effect and treatment effect within subgroups using predictive markers.

Conclusion: The simple themes provide a general guideline for evaluation of surrogate endpoints, prognostic markers, and predictive markers.

Keywords: Decision analysis; predictive marker; principal stratification; prognostic marker; randomized trial; relative utility curve; subgroup; treatment selection marker.

Figure 1

ROC and relative utility (RU) curves for predicting the risk of invasive breast cancer among asymptomatic women. Model 1 uses baseline variables. Model 2 uses baseline variables and breast density. The dashed vertical lines in the plot for the RU curve represent the range of risk thresholds in Tables 2 and 3. The figures are similar to those in Baker.

Figure 2

Schematic for modified adaptive signature design.

Figure 3

Subpopulation treatment effect pattern plot using data from prostate cancer classification (tumor or not), designating the first 6300 genes to be markers from a hypothetical randomization group 0 and the last 6300 genes to be the same markers from a hypothetical randomization group 1. The upper and lower dashed lines indicate the simultaneous 99% confidence interval.