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Neuro Oncol. 2016 May;18(5):609-23. doi: 10.1093/neuonc/nov255. Epub 2015 Dec 8.

Statistical considerations on prognostic models for glioma.

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Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, California (A.M.M., M.R.W.); Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California (A.M.M., M.R.W.); Institute of Human Genetics, University of California San Francisco, San Francisco, California (M.R.W.); Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J.); Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (J.E.E.-P.).


Given the lack of beneficial treatments in glioma, there is a need for prognostic models for therapeutic decision making and life planning. Recently several studies defining subtypes of glioma have been published. Here, we review the statistical considerations of how to build and validate prognostic models, explain the models presented in the current glioma literature, and discuss advantages and disadvantages of each model. The 3 statistical considerations to establishing clinically useful prognostic models are: study design, model building, and validation. Careful study design helps to ensure that the model is unbiased and generalizable to the population of interest. During model building, a discovery cohort of patients can be used to choose variables, construct models, and estimate prediction performance via internal validation. Via external validation, an independent dataset can assess how well the model performs. It is imperative that published models properly detail the study design and methods for both model building and validation. This provides readers the information necessary to assess the bias in a study, compare other published models, and determine the model's clinical usefulness. As editors, reviewers, and readers of the relevant literature, we should be cognizant of the needed statistical considerations and insist on their use.


glioma; model building; prognostic models; statistics; validation

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