Puzzler2 < Main < Vanderbilt Biostatistics Wiki

---+ Binary Response, Random Sample of 1000 Patients from the SUPPORT Study, Missing Data
Analyze the =[[http://biostat.mc.vanderbilt.edu/twiki/pub/Main/DataSets/support.html 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 <tt>load</tt>ed using the =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:
   1 Make a single chart showing proportions of deaths stratified by each of the other variables listed above
   1 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
   1 Initially estimate marginal relationships between continuous predictors and outcome using a nonparametric smoother
   1 Use marginal potential predictive discrimination of predictors to decide on how to spend degrees of freedom
   1 Impute missing lab data using [[http://biostat.mc.vanderbilt.edu/twiki/pub/Main/DataSets/support.html "most normal"]] values; impute =race= using the most frequent category (hint: see the =Hmisc impute= function)
   1 Fit a multivariable model with minimal observations deleted due to <tt>NA</tt>s
   1 Test partial effects of all predictors
   1 Graphically interpret the model three distinct ways
   1 Validate the model for discrimination and calibration ability

This topic: Main > WebHome > CE > Puzzlers > Puzzler2
Topic revision: 15 Aug 2006, AyumiShintani (raw view)

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