Six hundred simulated patients, shaped like the descriptive tables that motivate this package: a few continuous measurements crossed with several categorical variables worth stratifying on.
Format
A data frame with 600 rows and 11 columns:
- id
Patient identifier,
"P0001"to"P0600".- age
Age in years.
- sex
Factor:
"Male","Female".- site
Factor:
"Site A","Site B","Site C","Site D"."Site D"has no observations.- treatment
Factor:
"Control","Low dose","High dose".- severity
Factor:
"Mild","Moderate","Severe".- bmi
Body mass index, kg/m^2.
- crp
C-reactive protein, mg/L.
- los_days
Length of stay in days.
- fu_days
Days of follow-up, to death or to censoring. Censoring is by dropout or by the end of the study at 365 days.
- death
1if the patient died during follow-up,0if censored.
Details
Two features are deliberate. site declares a fourth level, "Site D",
that recruited nobody, so stratifying by site produces a stratum with
n = 0; the app lists it and draws no figure. severity has a small
"Severe" group – 20 patients against 381 and 199 – which is what the
minimum-n control is there to be tried on: it is drawn at the default
minimum of 10, and disappears from the figures, with a reason, as soon as
the minimum is raised past 20.
The data are simulated. They describe no real patients and support no clinical conclusion.
fu_days and death make the data usable for a Kaplan-Meier curve, and
age for the categorization controls.
Examples
str(epi_cohort)
#> 'data.frame': 600 obs. of 11 variables:
#> $ id : chr "P0001" "P0002" "P0003" "P0004" ...
#> $ age : num 66 78 57 42 57 64 64 67 72 69 ...
#> $ sex : Factor w/ 2 levels "Male","Female": 1 1 2 2 2 1 2 2 2 1 ...
#> $ site : Factor w/ 4 levels "Site A","Site B",..: 1 2 1 1 1 1 1 1 2 1 ...
#> $ treatment: Factor w/ 3 levels "Control","Low dose",..: 1 3 3 2 1 2 2 1 3 1 ...
#> $ severity : Factor w/ 3 levels "Mild","Moderate",..: 1 2 1 2 2 1 1 1 1 1 ...
#> $ bmi : num 23.6 23.1 24.9 20.1 25.3 21.2 23 25.6 27.6 26 ...
#> $ crp : num 4.4 4.6 8.8 21.8 18.1 17.6 4.4 6.3 1.7 3.4 ...
#> $ los_days : num 14 10 5 15 16 12 9 10 8 10 ...
#> $ fu_days : num 229 65 258 289 141 4 235 365 40 365 ...
#> $ death : int 1 1 0 0 1 0 0 0 0 0 ...
# The empty stratum that the app reports with n = 0.
table(epi_cohort$site)
#>
#> Site A Site B Site C Site D
#> 296 173 131 0