Prognostic Biomarker: provides information about the patients’ overall outcome, regardless of therapy.

Statistical test: is Marker X associated with an efficacy endpoint?

Predictive Biomarker: provides information about the effect of a therapeutic intervention; can be a target for therapy

Statistical test: is Marker X associated with the differential effect between treatments on an efficacy endpoint (treatment comparison)

Graphic illustration of prognostic and predictive biomarkers.

Statistical methods to evaluate a biomarker’s prognosis and prediction.

Clinical trial designs based on prognostic and predictive biomarkers

Prognostic Enrichment: to identify patients with a greater likelihood of having the event (or a large change in a continuous measure) of interest in a trial

Advantages: to increase the power of a study to detect any given level of risk reduction.

Predictive Enrichment: to identify patients more likely to respond to a particular intervention

Advantages: to better detect increased study efficiency or feasibility, or enhanced benefit-risk relationship

Define fit-for-purpose threshold on a biomarker with a continuous scale

To determine the optimization goal:
- the maximized differentiation in an outcome between marker selected and the un-selection populations by the cutoff
- the maximized or a targeted differentiation between treatments in a marker selected the population
- the maximized interaction effect between treatment and a categorized biomarker
- a target efficacy outcome, e.g. response rate, median survival
- an optimal sensitivity and specificity combination, e.g. Youden index by ROC
- prevalence consideration
- concordance with a reference biomarker