hw7key - BIOST/EPI 513 Spring Quarter 2011 Dr McKnight...

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1 BIOST/EPI 513 Spring Quarter 2011 Dr. McKnight HOMEWORK 7 KEY See Appendix I for STATA commands and Appendix II for output. 1. Using the data in prosHW.dta on the Assigments page of the class website, use the training data set (for which training == 1) and the command swaic (type findit swaic ) to improve upon the predictive model derived in class. The improvement will be made possible by allowing dummy variables for the dre variable (it was treated as grouped linear in class) and by also allowing a log(psa) term. (a) Generate a new variable equal to log(psa) , fit a logistic model using only the training d ata set to predict the binary “penetration” outcome using all the following variables: psa, log(psa), age, race, i.dre, i.dcaps, vol (main effects only). Present the value of the area under the ROC curve (AUC) for this model for both the training and validation data sets. AUC for the training data set: 0.787 AUC for the validation data set: 0.738 (b) Use swaic and the training data set to determine the model with a lowest AIC from among those considered. List the subset of the original variables that are included in this model. The model with the lowest AIC (209.9) contains log(psa) , dre (as a factor variable), vol , and race . (c) Fit the minimum AIC model derived using swaic in (b) to the training data set and present AIC and BIC for it.
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