Data from the SUPPORT study on whether right heart catheterization within 24 hours of admission to an intensive care unit affects survival (Connors et al., 1996). Catheterization was not randomized, so the comparison is confounded by how sick each patient was on admission, which is what the physiological covariates are for.
Format
A data frame with 1500 rows and 16 columns.
- rhc
whether the patient received right heart catheterization, 1 or 0. This is the treatment.
- death
whether the patient died during follow-up, 1 or 0.
- days
days from admission to death, or to last contact for a patient who did not die. Together with
deaththis is the survival outcome.- age
age in years.
- sex
"female"or"male".- race
"white","black"or"other".- edu
years of education.
- aps
APACHE III score on day 1, ignoring coma. Higher is sicker.
- meanbp
mean blood pressure on day 1.
- resp
respiratory rate on day 1.
- hema
hematocrit on day 1.
- pafi
ratio of arterial oxygen to inspired oxygen on day 1.
- paco2
arterial carbon dioxide on day 1.
- crea
serum creatinine on day 1.
- surv2m
the study's own model-based estimate, made on day 1, of the probability of surviving two months.
- card
whether cardiovascular disease was a diagnosis,
"no"or"yes".
Source
Assembled from https://hbiostat.org/data/repo/rhc.csv by
data-raw/rhc.R.
Details
The outcome appears in two forms. death is whether the patient died during
follow-up, and days is how long that took, so the same event supports a
binary analysis that ignores timing and a right-censored survival analysis
that does not. Note that a patient who did not die is censored at their last
contact.
These are a random 1500 of the 5735 patients in the original file. The covariates are the thirteen used in the worked example at https://iqss.github.io/dss-ps/example.html, all recorded before catheterization; the full study collected many more.
The treatment and the outcome are coded 0 and 1 rather than as factors, so that a contrast between them is a single number rather than one per level.
Nothing here makes the causal assumptions hold. Whether the effect of rhc
on death can be read causally depends on whether these covariates account
for how patients were selected for catheterization, which is a question about
the study rather than about any model. See vignette("causal").
References
Connors, A. F., Speroff, T., Dawson, N. V., et al. (1996). The effectiveness of right heart catheterization in the initial care of critically ill patients. JAMA, 276(11), 889–897. doi:10.1001/jama.1996.03540110043030
Examples
data("rhc")
# The treatment against the binary outcome
table(rhc$rhc, rhc$death)
#>
#> 0 1
#> 0 356 579
#> 1 163 402
# The covariates the confounding runs through, all recorded on admission
summary(rhc[c("age", "aps", "meanbp", "surv2m")])
#> age aps meanbp surv2m
#> Min. : 18.19 Min. : 4.00 Min. : 0.00 Min. :0.000
#> 1st Qu.: 50.22 1st Qu.: 41.00 1st Qu.: 50.00 1st Qu.:0.463
#> Median : 63.96 Median : 54.00 Median : 63.00 Median :0.623
#> Mean : 61.42 Mean : 55.41 Mean : 78.36 Mean :0.587
#> 3rd Qu.: 73.86 3rd Qu.: 68.00 3rd Qu.:113.00 3rd Qu.:0.747
#> Max. :100.25 Max. :147.00 Max. :222.00 Max. :0.940
# The same event as a survival outcome, a patient who did not die being
# censored at their last contact
if (rlang::is_installed("survival")) {
with(rhc, summary(survival::Surv(days, death)))
}
#> time status
#> Min. : 2.0 Min. :0.000
#> 1st Qu.: 16.0 1st Qu.:0.000
#> Median : 149.0 Median :1.000
#> Mean : 182.6 Mean :0.654
#> 3rd Qu.: 230.0 3rd Qu.:1.000
#> Max. :1793.0 Max. :1.000