Fig 3.

The value of a marker for targeting of treatment depends on its influence both on outcome risk and on relative treatment effect. The domain along the x axis quantifies prognostic effects; the range along the y axis quantifies relative effect modification (sometimes called “predictive” effects). The clinically significant effect measure (absolute risk difference or number needed to treat (NNT)) is depicted by the contour plot. The average effect in the overall trial is shown by the large red dot, which can be disaggregated into subgroups (shown by the smaller black and white dots) in different ways. Both pure prognostic markers (which scatter patient subgroups horizontally) and pure relative effect modifying (“predictive”) markers (which scatter patient subgroups vertically) help discriminate patient groups with different degrees of absolute benefit. Asymmetry of the scatter represents the usual non-normal distribution of risk (here shown as log normal, with a greater number of low risk and low benefit patients). Generally, “predictive” markers are more difficult to identify than prognostic markers, both because reliable information about effect modifiers is usually scant and because power to examine treatment effect interactions is substantially lower than prognostic effects. However, factors are often both prognostic and relative effect modifying, and these effects may be “synergistic” (relative risk reduction and outcome risk positively correlated) or “antagonistic” (relative risk reduction and outcome risk negatively correlated). The most useful factor for treatment selection is that for which the absolute risk difference most varies as a function of that factor’s value (here, the “synergistic” example). This corresponds to improved discrimination for treatment benefit on the risk difference scale. Note that for the factor with antagonistic effects, patients with the largest relative treatment effect paradoxically benefit the least on the absolute scale. From a decision analytic perspective, the clinical value of the marker is determined by its ability to distribute patients across a decisionally important threshold, which depends on the treatment burden (accounting for patient preferences, adverse effects, and costs). These decision thresholds are represented by the contours