- #POSITIVE PREDICTIVE VALUE SEQUENTIAL TESTING UPGRADE#
- #POSITIVE PREDICTIVE VALUE SEQUENTIAL TESTING TRIAL#
#POSITIVE PREDICTIVE VALUE SEQUENTIAL TESTING UPGRADE#
According to Chun, a significant upgrade occurs whenever the Gleason sum changes from ≤ 6 at biopsy to ≥ 7 at RP or from 7 at biopsy to ≥ 8 at RP. To motivate and illustrate the method, we considered a nomogram developed by Chun 3 for predicting significant upgrading in Gleason scoring from biopsy to radical prostatectomy (RP). The method allows the clinician to continually monitor the performance of the technology until sufficient evidence has accumulated to make a reliable decision whether or not to adopt the technology. In this article we introduce an easy to use method for the in-clinic validation of new prediction technologies. Ultimately this practice will benefit the patient as well. Such benefits then lead to greater confidence in a truly effective technology or to the prudent decision to not use an ineffective technology. The benefits to be conferred by such assessment come from having confirmation or refutation of the purported prediction accuracies of new technologies when applied to their own patients. Given this recent evidence about the potential local nature of prediction technologies, it is apparent that cancer clinicians would benefit from the routine assessment of new predictive technologies in their own population.
#POSITIVE PREDICTIVE VALUE SEQUENTIAL TESTING TRIAL#
However, in our study the predicted prognosis for recurrence-free survival by the nomogram was worse than the outcomes actually observed in many of our patients, highlighting the caution that needs to be exercised when routinely applying the nomogram…The discrepancy also has important implications that go beyond patient counseling, as it may influence a clinician's decision as to whether or not to include a patient in a clinical trial of adjuvant therapy.” 2 The performance of the prediction model was much lower than anticipated, leading the authors to comment: “Nomograms are currently exerting a strong influence on clinical practice. Hupertan 2 tested a prediction model for renal cell carcinoma recurrence in a population very different from the one used to create and initially validate it. For example, Reid et al 1 recently failed to validate a predictive model using microarray data for breast cancer clinical treatment outcomes on an independent cohort of patients. Recently published research shows that cancer clinicians should assess the validity of even well-validated predictive technologies (tests, statistical models, and nomograms) in their own clinical population.