On Statistical Approaches for the Multivariable Analysis of Prognostic Marker Studies

摘要

Various statistical methods to analyse prognostic marker studies are available and are used in practice. Issues of multivariable model building will be discussed in the framework of regression models and classification and regression trees (CART). It is shown that the choice of one specific statistical method has a strong influence on the results and, therefore, on the interpretation of a prognostic marker. Within regression models we compare the full model with models obtained by backward variable selection considering also transformations of continuous covariates. We discuss problems caused by the uncritical application of CART and outline advantages of small and simple trees. Furthermore, we show how to form risk groups with different prognoses and we illustrate the necessity to validate results in an independent study. Data of two breast cancer studies are used for illustration.