Combining a molecular profile with a clinical and pathological profile: Biostatistical considerations

The use of molecular markers and gene expression profiling provides a promising approach for improving the predictive accuracy of current prognostic indices for predicting which patients with non-muscle-invasive bladder cancer will progress to muscle-invasive disease. There are many statistical pitfalls in establishing the benefit of a multigene expression classifier during its development. First, there are issues related to the identification of the individual genes and the false discovery rate, the instability of the genes identified and their combination into a classifier. Secondly, the classifier should be validated, preferably on an independent data set, to show its reproducibility. Next, it is necessary to show that adding the classifier to an existing model based on the most important clinical and pathological factors improves the predictive accuracy of the model. This cannot be determined based on the classifier's hazard ratio or p-value in a multivariate model, but should be assessed based on an improvement in statistics such as the area under the curve and the concordance index. Finally, nomograms are superior to stage and risk group classifications for predicting outcome, but the model predicting the outcome must be well calibrated. It is important for investigators to be aware of these pitfalls in order to develop statistically valid classifiers that will truly improve our ability to predict a patient's risk of progression