Comparison of Predicted Probabilities of Proportional Hazards Regression and Linear Discriminant Analysis Methods Using a Colorectal Cancer Molecular Biomarker Database

Background: Although a majority of studies in cancer biomarker discovery claim to use proportional hazards regression (PHREG) to the study the ability of a biomarker to predict survival, few studies use the predicted probabilities obtained from the model to test the quality of the model. In this paper, we compared the quality of predictions by a PHREG model to that of a linear discriminant analysis (LDA) in both training and test set settings. Methods: The PHREG and LDA models were built on a 491 colorectal cancer (CRC) patient dataset comprised of demo-graphic and clinicopathologic variables, and phenotypic expression of p53 and Bcl-2. Two variable selection methods, stepwise discriminant analysis and the backward selection, were used to identify the fi nal models. The endpoint of predic-tion in these models was fi ve-year post-surgery survival. We also used linear regression model to examine the effect of bin size in the training set on the accuracy of prediction in the test set. Results: The two variable selection techniques resulted in different models when stage was included in the list of variables available for selection. However, the proportion of survivors and non-survivors correctly identifi ed was identical in both o