Risk prediction models for binary response variables for the coronary bypass operation

Hongbin Zhang
The ability to predict 30 day operative mortality and complications following coronary artery bypass surgery in the individual patient has important implications clinically and for the design of clinical trials. This thesis focuses on setting up risk stratification algorithms. Utilizing the binary feature of the response variables, logistic regression analyses and classification trees (recursive partitioning) were used with the variables identified by the Health Data Research Institute in Portland, Oregon. The data set contains records...
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