R/data.R
support.RdThe SUPPORT dataset tracks four response variables: hospital death, severe functional disability, hospital costs, and time until death and death itself. The patients are followed for up to 5.56 years. Data included only tracks follow-up time and death.
supportA dataframe with 9104 observations and 34 variables after imputation and the removal of response variables like hospital charges, patient ratio of costs to charges and micro-costs. Ordinal variables, namely functional disability and income, were also removed. Finally, Surrogate activities of daily living were removed due to sparsity. There were 6 other model scores in the data-set and they were removed; only aps and sps were kept.
Stores a double representing age.
Death at any time up to NDI date: 31DEC94.
0=female, 1=male.
Days from study entry to discharge.
days of follow-up.
Each level of dzgroup: ARF/MOSF w/Sepsis, COPD, CHF, Cirrhosis, Coma, Colon Cancer, Lung Cancer, MOSF with malignancy.
ARF/MOSF, COPD/CHF/Cirrhosis, Coma and cancer disease classes.
the number of comorbidities.
years of education of patient.
The SUPPORT coma score based on Glasgow D3.
Average TISS, days 3-25.
Indicates race. White, Black, Asian, Hispanic or other.
Day in Hospital at Study Admit
Diabetes (Com 27-28, Dx 73)
Dementia (Comorbidity 6)
Cancer State
Mean Arterial Blood Pressure Day 3.
White blood cell count on day 3.
Heart rate day 3.
Respiration Rate day 3.
Temperature, in Celsius, on day 3.
PaO2/(0.01*FiO2) Day 3.
Serum albumin day 3.
Bilirubin Day 3.
Serum creatinine day 3.
Serum sodium day 3.
Serum pH (in arteries) day 3.
Serum glucose day 3.
BUN day 3.
urine output day 3.
ADL patient day 3.
Imputed ADL calibrated to surrogate, if a surrogate was used for a follow up.
SUPPORT physiology score
Apache III physiology score
Available at the following website: https://biostat.app.vumc.org/wiki/Main/SupportDesc. note: must unzip and process this data before use.
Some of the original data was missing. Before imputation, there were
a total of 9105 individuals and 47 variables. Of those variables, a few
were removed before imputation. We removed three response variables:
hospital charges, patient ratio of costs to charge,s and patient
micro-costs. Next, we removed hospital death as it was directly informative
of our event of interest, namely death. We also removed functional
disability and income as they are ordinal covariates. Finally, we removed 8
covariates related to the results of previous findings: we removed SUPPORT
day 3 physiology score (sps), APACHE III day 3 physiology score
(aps), SUPPORT model 2-month survival estimate, SUPPORT model
6-month survival estimate, Physician's 2-month survival estimate for pt.,
Physician's 6-month survival estimate for pt., Patient had Do Not
Resuscitate (DNR) order, and Day of DNR order (<0 if before study). Of
these, sps and aps were added on after imputation, as they
were missing only 1 observation. First we imputed manually using the normal
values for physiological measures recommended by Knaus et al. (1995). Next,
we imputed a single dataset using mice with default settings. After
imputation, we noted that the covariate for surrogate activities of daily
living was not imputed. This is due to collinearity between the other two
covariates for activities of daily living. Therefore, surrogate activities
of daily living was removed.
Knaus WA, Harrell FE, Lynn J et al. (1995): The SUPPORT prognostic model: Objective estimates of survival for seriously ill hospitalized adults. Annals of Internal Medicine 122:191-203. doi:10.7326/0003-4819-122-3-199502010-00007 .
http://biostat.mc.vanderbilt.edu/wiki/Main/SupportDesc
http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/Csupport.html
data("support")
# Using the matrix interface and log of time
x <- model.matrix(death ~ . - d.time - 1, data = support)
y <- with(support, cbind(death, d.time))
fit_cb <- casebase::fitSmoothHazard.fit(x, y, time = "d.time",
event = "death",
formula_time = ~ log(d.time),
ratio = 1)