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Survival Analysis: A Self-Learning Text David G. Kleinbaum, Mitchel Klein
Publisher: Springer

Kleinbaum of the second edition of Survival Analysis-A Self-Learning Text. Applied longitudinal data analysis: Modeling change and event occurrence New York, USA: Oxford University Press. A handbook of test construction. Our method for discovering prognostic signatures builds on top of a human protein functional interaction (FI) network constructed by combining curated and uncurated data sources using a machine learning technique [24]. We then apply this expression matrix for the superpc analysis [23] to search for linear combinations of network modules that are significantly correlated with patient survival or other clinically relevant criteria. Loading Saturday, May 21, 2011. Kleinbaum DG, Klein M: Survival analysis: a self-learning text. This FI network covers roughly each tissue in the series. Survival Analysis: A Self-Learning Text – Data files here. Survival analysis: A self-learning text (2nd ed.). Survival analysis: a self-learning text By David G. Survival Analysis – See Klein's page for data sets and errors. Survival analysis: A self-learning text. Aly C: Filtration rates of mosquito larvae in suspensions of latex microspheres and yeast cells. Klein, Survival Analysis: A Self-Learning Text, Springer, New York, NY, USA, 2nd edition, 2005. Designed and taught epidemiologic methods physicians at Emory's Master of Science in Clinical Research Program.