Judging New Markers by Their Ability to Improve Predictive Accuracy

Abstract

The man who has recently received a radical prostatectomy to treat his clinically localized prostate cancer now faces an important decision: whether or not adjuvant therapy would be beneficial. Clearly, a major factor in this decision is the likelihood of his disease recurring in the absence of additional therapy. There are at least three well-documented prognostic models for use in this setting, and each predicts the likelihood of biochemical progression (i.e., prostate-specific antigen [PSA]-defined recurrence of prostate cancer). Partin et al. (1) developed an equation they called “Rw”; Blute et al. (2) devised the “GPSM” score (which includes the Gleason score, PSA level, seminal vesicle status, and margin status), and Kattan et al. (3) derived a postoperative nomogram, which was later validated by Graefen et al. (4). Which of these models predicts best for the individual patient? The GPSM score and the postoperative nomogram have been evaluated by the concordance index and have values of 0.76 and 0.80, respectively, suggesting relatively similar performance. The concordance index is the probability that, given two randomly selected patients, the patient with the worse outcome is, in fact, predicted to have a worse outcome (5). This measure, similar to an area under the receiver operating characteristic curve, ranges from 0.5 (i.e., chance or a coin flip) to 1.0 (perfect ability to rank patients).