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`support`

dataset available at http://biostat.mc.vanderbilt.edu/twiki/pub/Main/DataSets/support.sav (an R `save`

file that can also be downloaded and `Hmisc getHdata`

function) to develop a model to predict the probability that a patient dies in the hospital. Consider the following predictors: `age, sex, dzgroup, num.co, scoma, race, meanbp, hrt, temp, pafi, alb`

. As part of your analysis do the following: - Make a single chart showing proportions of deaths stratified by each of the other variables listed above
- Characterize patterns of missing values in the predictors by plotting missingness tendencies of single predictors and jointly of two predictors at a time, and by using recursive partitioning to determine what kind of patients tended to have a higher proportion of missing measurements for the predictor that is missing most often
- Initially estimate marginal relationships between continuous predictors and outcome using a nonparametric smoother
- Use marginal potential predictive discrimination of predictors to decide on how to spend degrees of freedom
- Impute missing lab data using "most normal" values; impute
`race`

using the most frequent category (hint: see the`Hmisc impute`

function) - Fit a multivariable model with minimal observations deleted due to
`NA`s - Test partial effects of all predictors
- Graphically interpret the model three distinct ways
- Validate the model for discrimination and calibration ability

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Topic revision: r3 - 15 Aug 2006, AyumiShintani

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