R/Data.R
support.RdThe SUPPORT dataset tracks five 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. See Bhatnagar et al. (2020) for details.
data(support)A data frame with 9,104 observations and 34 variables after imputation and the removal of response variables like hospital charges, patient ratio of costs to charges and micro-costs following Bhatnagar et al. (2020). 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 (National Death Index) date: 12/31/1994.
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 patients.
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 (Com27-28, 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.
Some of the original data was missing. Before imputation, there were
a total of 9,104 individuals and 47 variables. Following Bhatnagar et al. (2020), a few variables
were removed. Three response variables were removed:
hospital charges, patient ratio of costs to charges and patient
micro-costs. Hospital death was also removed as it was directly informative
of the event of interest, namely death. Additionally, functional disability and
income were removed as they are ordinal covariates. Finally, 8
covariates were removed related to the results of previous findings: 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 the imputation is done manually using the normal
values for physiological measures recommended by Knaus et al. (1995). Next,
a single dataset was imputed using mice with default settings. After
imputation, 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 were removed. See details in the R package (casebase) by Bhatnagar et al. (2020).
Bhatnagar, S., Turgeon, M., Islam, J., Hanley, J. A., and Saarela, O. (2020) casebase: Fitting Flexible Smooth-in-Time Hazards and Risk Functions via Logistic and Multinomial Regression. R package version 0.9.0, https://CRAN.R-project.org/package=casebase.
Knaus, W. A., Harrell, F. E., Lynn, J., Goldman, L., Phillips, R. S., Connors, A. F., et al. (1995)
The SUPPORT prognostic model: Objective estimates of survival for seriously ill hospitalized adults.
Annals of Internal Medicine, 122(3): 191-203.
if (FALSE) {
data(support)
support <- support[support$ca %in% c("metastatic"),]
time <- support$d.time
death <- support$death
diabetes <- model.matrix(~factor(support$diabetes))[,-1]
#sex: female as the reference group
sex <- model.matrix(~support$sex)[,-1]
#age: continuous variable
age <-support$age
age[support$age<=50] <- "<50"
age[support$age>50 & support$age<=60] <- "50-59"
age[support$age>60 & support$age<70] <- "60-69"
age[support$age>=70] <- "70+"
age <- factor(age, levels = c("60-69", "<50", "50-59", "70+"))
z_age <- model.matrix(~age)[,-1]
z <- data.frame(z_age, sex, diabetes)
colnames(z) <- c("age_50", "age_50_59", "age_70", "diabetes", "male")
library(survminer)
library(survival)
data <- data.frame(time, death, z)
fit1 <- survfit(Surv(time, death) ~ diabetes, data = data)
fit2 <- survfit(Surv(time, death) ~ age_50 + age_50_59 + age_70, data = data)
ggsurvplot(fit1, data = data)
ggsurvplot(fit2, data = data, legend.labs = c("60-69", "70+", "50-59", "<50"))
fit.coxtv <- coxtv(event = death, z = z, time = time)
}