The bootstrap and identification of prognostic factors via Cox's proportional hazards regression model.

This paper describes the use of the bootstrap, a new computer-based statistical methodology, to help validate a regression model resulting from the fitting of Cox's proportional hazards model to a set of censored survival data. As an example, we define a prognostic model for outcome in childhood acute lymphocytic leukemia with the Cox model and use of a training set of 224 patients. To validate the accuracy of the model, we use a bootstrap resampling technique to mimic the population under study in two stages. First, we select the important prognostic factors via a stepwise regression procedure with 100 bootstrap samples. Secondly we estimate the corresponding regression parameters for these important factors with 400 bootstrap samples. The bootstrap result suggests that the model constructed from the training set is reasonable.

Duke Scholars

Author Stephen L. George Biostatistics & Bioinformatics, Division of Biostatistics