The goal of this article is to explain the differences between prognostic and predictive markers and to describe how to make this distinction based on clinical data and formal statistical testing. The term biomarker refers to a measurement variable that is associated with disease outcome. It can be a single measurement, such as prostate-specific antigen (PSA) level, or a classifier (signature) computed from measures of numerous other variables, such as OncoType DX recurrence score, 1 which is calculated from the measurements of the expression levels of 21 genes. There is considerable confusion about the distinction between a predictive biomarker and a prognostic biomarker. Confusion even exists among biostat-isticians because they have been taught predictive modeling as part of their training. A predictive model is a mathematical relationship between explanatory (independent) variables and an outcome (dependent) variable with the goal of predicting a future outcome based on the values of the explanatory variables in the model. An example of a predictive model is a nomogram that predicts the probability a man will not die of prostate cancer (outcome variable) within 10 years of undergoing a radical prostatectomy. 2 This model's explanatory variables (biomarkers) are age, PSA level, tumor Gleason score, tumor clinical stage, and number ofpositive biopsy cores and number of negative biopsy cores at time of diagnosis. The explanatory variables in a predictive model are often prognostic, but statisticians may refer to them as predictive variables, which may generate confusion.
ASJC Scopus subject areas
- Cancer Research