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The bootstrap and identification of prognostic factors via cox's proportional hazards regression model
Author(s) -
Chen ChenHsin,
George Stephen L.
Publication year - 1985
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780040107
Subject(s) - proportional hazards model , resampling , statistics , regression analysis , regression , population , bootstrap model , computer science , data set , linear regression , econometrics , mathematics , medicine , boson , physics , environmental health , particle physics , particle decay
This paper describes the use of the bootstrap, 1,2 a new computer‐based statistical methodology, to help validate a regression model resulting from the fitting of Cox's proportional hazards model 3 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.