Sequential testing approach as an efficient and easier alternative for the validation of new predictive technologies in the clinic

Abstract

Purpose When clinicians contemplate the use of a new predictive technology in their practice, such as a nomogram, there is always a question of whether the new test is beneficial to their own clinical population. Unfortunately, traditional validation methods require a large number of subjects for validation testing and delay the decision-making process. We present an efficient and easy-to-use method based on the concept of sequential data analysis. Patients and Methods We illustrate with an example determining the validity of a technology for predicting Gleason score upgrading from biopsy to postprostatectomy (the Chun nomogram) in a clinical population different from the one used to initially validate the technology. Clinical data required by the Chun nomogram were available from 201 patients from the Cooperative Prostate Cancer Tissue Resource. Results Of 124 patients predicted by the Chun nomogram to have an upgrading event, 47 actually did. The positive predictive value (PPV) of the model was therefore 38% and significantly (P