`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

Copyright © 2013-2022 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.

Ideas, requests, problems regarding Vanderbilt Biostatistics Wiki? Send feedback

Ideas, requests, problems regarding Vanderbilt Biostatistics Wiki? Send feedback