View the Current Patient Population Case Mix Compared to the Major Trauma Study Case Mix
Source:R/trauma_case_mix.r
trauma_case_mix.RdThis function compares the current patient population's case mix (based on probability of survival, Ps) to the MTOS case mix by binning patients into specific Ps ranges. It returns the fraction of patients in each range and compares it to the MTOS distribution. For more information on the methods used in these calculations, please see Flora (1978) and Boyd et al. (1987).
Value
A data frame containing:
Ps_range: The probability of survival range category.current_fraction: The fraction of patients in the current dataset within each Ps range.MTOS_distribution: The reference distribution of patients in each Ps range based on the MTOS study.survivals: The number of observed survivors (outcome = 1) in each Ps range.predicted_survivals: The sum of predicted survivals (sum of Ps values) in each Ps range.deaths: The number of observed deaths (outcome = 0) in each Ps range.predicted_deaths: The sum of predicted deaths (sum of 1 - Ps values) in each Ps range.count: The total number of patients in each Ps range.
Details
The function checks whether the outcome_col contains values representing a
binary outcome. It also ensures that Ps_col contains numeric values within
the range 0 to 1. If any values exceed 1, a warning is issued. The patients
are then grouped into predefined Ps ranges, and the function compares the
fraction of patients in each range with the MTOS case mix distribution.
Like other statistical computing functions, trauma_case_mix() is happiest
without missing data. It is best to pass complete probability of survival
and outcome data to the function for optimal performance. With smaller
datasets, this is especially helpful. However, trauma_case_mix() will
throw a warning about missing values, if any exist in Ps_col and/or
outcome_col.
Note
This function will produce the most reliable and interpretable results when using a dataset that has one row per patient, with each column being a feature.
Examples
# Generate example data
set.seed(123)
# Parameters
# Total number of patients
n_patients <- 5000
# Arbitrary group labels
groups <- sample(x = LETTERS[1:2], size = n_patients, replace = TRUE)
# Trauma types
trauma_type_values <- sample(
x = c("Blunt", "Penetrating"),
size = n_patients,
replace = TRUE
)
# RTS values
rts_values <- sample(
x = seq(from = 0, to = 7.8408, by = 0.005),
size = n_patients,
replace = TRUE
)
# patient ages
ages <- sample(
x = seq(from = 0, to = 100, by = 1),
size = n_patients,
replace = TRUE
)
# ISS scores
iss_scores <- sample(
x = seq(from = 0, to = 75, by = 1),
size = n_patients,
replace = TRUE
)
# Generate survival probabilities (Ps)
Ps <- traumar::probability_of_survival(
trauma_type = trauma_type_values,
age = ages,
rts = rts_values,
iss = iss_scores
)
# Simulate survival outcomes based on Ps
survival_outcomes <- rbinom(n_patients, size = 1, prob = Ps)
# Create data frame
data <- data.frame(Ps = Ps, survival = survival_outcomes, groups = groups) |>
dplyr::mutate(death = dplyr::if_else(survival == 1, 0, 1))
# Compare the current case mix with the MTOS case mix
trauma_case_mix(data, Ps_col = Ps, outcome_col = death)
#> Ps_range current_fraction MTOS_distribution survivals predicted_survivals
#> 1 0.00 - 0.25 0.5414 0.010 2534 187.9191
#> 2 0.26 - 0.50 0.1432 0.043 468 269.5871
#> 3 0.51 - 0.75 0.1278 0.000 217 405.8877
#> 4 0.76 - 0.90 0.0870 0.052 58 366.1312
#> 5 0.91 - 0.95 0.0508 0.053 18 237.6390
#> 6 0.96 - 1.00 0.0498 0.842 4 243.4833
#> deaths predicted_deaths count
#> 1 173 2519.080869 2707
#> 2 248 446.412896 716
#> 3 422 233.112251 639
#> 4 377 68.868790 435
#> 5 236 16.361033 254
#> 6 245 5.516716 249