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 13 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.
NAfor 122 patients who were not tested, more often the milder ones.- 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.- admit_date
Date of admission, over 2021 to 2023. Moderate and severe cases cluster in the winter, mild ones in the summer.
- svy_weight
Survey weight: the number of admissions each patient stands for, larger for the under-sampled mild presentations. Sums to about 50,000.
Details
Three 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. crp is the only column with missing
values, and they are not missing at random: the milder the presentation,
the more often the measurement was left undone, so 105 of the 381 mild
patients have no CRP against none of the 20 severe ones. Deriving missing
vs observed from crp and describing another variable within it is what
that pattern is there for.
The data are simulated. They describe no real patients and support no clinical conclusion.
admit_date is the fourth of these. It spans three calendar years and has
several hundred distinct values, so it cannot be stratified on as it stands
– that is the point of it. Read at a resolution first, by season or by
month of the year, it becomes a variable with four or twelve levels that can
be used as a layer like any other. The seasonality is in the case mix rather
than in the number of admissions: moderate and severe presentations cluster
in the winter and mild ones spread into the summer, so a season carries a
real difference in crp and in los_days rather than noise.
svy_weight is the fifth. It reads the cohort as a sample of some 50,000
admissions in which severe presentations were over-sampled and mild ones
under-sampled, and gives each patient the number of admissions they stand
for. Set it as the survey weight and the weighted cohort is mostly mild, as
the admissions were: a weighted mean of crp and a weighted proportion of
death come out below the unweighted ones. site can serve as the
sampling strata to try those controls with it.
fu_days and death make the data usable for a Kaplan-Meier curve, age
for the methods that cut a continuous variable into groups, crp for the
one that splits a variable by whether it has a value, and admit_date for
the one that reads a date at a chosen resolution.
Examples
str(epi_cohort)
#> 'data.frame': 600 obs. of 13 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 NA 21.8 18.1 17.6 4.4 NA NA NA ...
#> $ 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 ...
#> $ admit_date: Date, format: "2021-11-05" "2021-03-24" ...
#> $ svy_weight: num 110 32.2 102.1 34.5 32 ...
# The empty stratum that the app reports with n = 0.
table(epi_cohort$site)
#>
#> Site A Site B Site C Site D
#> 296 173 131 0
# Who was not tested, by how ill they were.
table(epi_cohort$severity, is.na(epi_cohort$crp))
#>
#> FALSE TRUE
#> Mild 276 105
#> Moderate 182 17
#> Severe 20 0
# Too many distinct dates to stratify on, until it is read at a resolution.
length(unique(epi_cohort$admit_date))
#> [1] 475
table(factor(month.abb[data.table::month(epi_cohort$admit_date)],
levels = month.abb))
#>
#> Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
#> 48 54 60 41 49 51 58 47 45 47 51 49