support {casebase} | R Documentation |
Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT)
Description
The 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.
Usage
support
Format
A 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.
- Age
Stores a double representing age.
- death
-
Death at any time up to NDI date: 31DEC94.
- sex
0=female, 1=male.
- slos
Days from study entry to discharge.
- d.time
days of follow-up.
- dzgroup
Each level of dzgroup: ARF/MOSF w/Sepsis, COPD, CHF, Cirrhosis, Coma, Colon Cancer, Lung Cancer, MOSF with malignancy.
- dzclass
ARF/MOSF, COPD/CHF/Cirrhosis, Coma and cancer disease classes.
- num.co
the number of comorbidities.
- edu
years of education of patient.
- scoma
The SUPPORT coma score based on Glasgow D3.
- avtisst
Average TISS, days 3-25.
- race
Indicates race. White, Black, Asian, Hispanic or other.
- hday
Day in Hospital at Study Admit
- diabetes
Diabetes (Com 27-28, Dx 73)
- dementia
Dementia (Comorbidity 6)
- ca
Cancer State
- meanbp
Mean Arterial Blood Pressure Day 3.
- wblc
-
White blood cell count on day 3.
- hrt
Heart rate day 3.
- resp
Respiration Rate day 3.
- temp
Temperature, in Celsius, on day 3.
- pafi
PaO2/(0.01*FiO2) Day 3.
- alb
-
Serum albumin day 3.
- bili
Bilirubin Day 3.
- crea
Serum creatinine day 3.
- sod
Serum sodium day 3.
- ph
Serum pH (in arteries) day 3.
- glucose
Serum glucose day 3.
- bun
-
BUN day 3.
- urine
urine output day 3.
- adlp
ADL patient day 3.
- adlsc
Imputed ADL calibrated to surrogate, if a surrogate was used for a follow up.
- sps
SUPPORT physiology score
- aps
Apache III physiology score
Details
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.
Source
Available at the following website: https://biostat.app.vumc.org/wiki/Main/SupportDesc. note: must unzip and process this data before use.
References
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
Examples
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)