View source: R/non_linear_bins.R
nonlinear_bins | R Documentation |
This function generates nonlinear bins for probability of survival data based on specified thresholds and divisors as specified in Napoli et al. (2017), Schroeder et al. (2019), and Kassar et al. (2016). This function calculates bin statistics, including mean, standard deviation, total alive, total dead, count, and percentage for each bin.
nonlinear_bins(
data,
Ps_col,
outcome_col,
group_vars = NULL,
divisor1 = 5,
divisor2 = 5,
threshold_1 = 0.9,
threshold_2 = 0.99
)
data |
A |
Ps_col |
The name of the column containing the survival probabilities (Ps). Should be numeric on a scale from 0 to 1. |
outcome_col |
The name of the column containing the outcome data. It
should be binary, with values indicating patient survival. A value of |
group_vars |
Optional grouping variables for bin statistics calculations. These should be specified as quoted column names. |
divisor1 |
A parameter to control the width of the probability of
survival range bins. Affects the creation of step sizes for the beginning
of each bin range. Defaults to |
divisor2 |
A parameter to control the width of the probability of
survival range bins. Affects the creation of step sizes for the beginning
of each bin range. Defaults to |
threshold_1 |
A parameter to decide where data indices will begin to
create step sizes. Defaults to |
threshold_2 |
A parameter to decide where data indices will end to
create step sizes. Defaults to |
Like other statistical computing functions, nonlinear_bins()
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, nonlinear_bins()
will throw
a warning about missing values, if any exist in Ps_col
and/or
outcome_col
.
nonlinear_bins()
assumes Ps_col
contains probabilities derived from
real-world inputs for the Trauma Injury Severity Score (TRISS) model.
Synthetic or low-variability data (especially with small sample sizes) may
not reflect the distribution of TRISS-derived survival probabilities. This
can result in unstable estimates or function failure due to insufficient
dispersion. With small sample sizes, it may be important to use smaller
values with the divisor arguments and adjust the thresholds (based on the
distribution of the Ps_col
values) to create bins that better accommodate
the data.
By default, nonlinear_bins()
derives bin cut points from the full dataset’s
distribution. This ensures comparability across groups when group_vars
is
used. To tailor binning to a specific group (e.g., a single hospital), filter
the dataset to that subgroup before calling nonlinear_bins()
. The function
will then compute bins and related statistics using only that subset’s
Ps_col
distribution. When group_vars
is used, and ff a group lacks
observations within one or more bins, rm_bin_summary()
will compute
statistics only for the bins that contain data. Bins with no observations are
excluded from the summary for that group.
A list with two elements:
intervals
: A vector defining bin boundaries for probability of
survival.
bin_stats
: A tibble
containing:
bin_number
: Bin index.
bin_start
, bin_end
: Bin range.
mean
, sd
: Mean and standard deviation of Ps_col
within the bin.
Pred_Survivors_b
, Pred_Deaths_b
: Predicted counts of survivors and
decedents, respectively.
AntiS_b
, AntiM_b
: Anticipated proportion survived, and deceased,
respectively.
alive
, dead
: Count of observed survivors and non-survivors.
count
: Total records in the bin.
percent
: Percentage of total records within each bin.
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.
The mean
and AntiS_b
are approximately equivalent in this context. They
are kept in the output for clarity.
Nicolas Foss, Ed.D, MS, original implementation in MATLAB by Nicholas J. Napoli, Ph.D., MS
Kassar, O.M., Eklund, E.A., Barnhardt, W.F., Napoli, N.J., Barnes, L.E., Young, J.S. (2016). Trauma survival margin analysis: A dissection of trauma center performance through initial lactate. The American Surgeon, 82(7), 649-653. doi:10.1177/000313481608200733
Napoli, N. J., Barnhardt, W., Kotoriy, M. E., Young, J. S., & Barnes, L. E. (2017). Relative mortality analysis: A new tool to evaluate clinical performance in trauma centers. IISE Transactions on Healthcare Systems Engineering, 7(3), 181–191. doi:10.1080/24725579.2017.1325948
Schroeder, P. H., Napoli, N. J., Barnhardt, W. F., Barnes, L. E., & Young, J. S. (2018). Relative mortality analysis of the “golden hour”: A comprehensive acuity stratification approach to address disagreement in current literature. Prehospital Emergency Care, 23(2), 254–262. doi:10.1080/10903127.2018.1489021
probability_of_survival()
, rmm()
, and rm_bin_summary()
# 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))
# Apply the nonlinear_bins function
results <- nonlinear_bins(
data = data,
Ps_col = Ps,
outcome_col = survival,
divisor1 = 4,
divisor2 = 4,
threshold_1 = 0.9,
threshold_2 = 0.99
)
# View results
results$intervals
results$bin_stats
# Example with grouping by a categorical variable
# Run the function using a single grouping variable
results_grouped <- nonlinear_bins(
data,
Ps_col = Ps,
outcome_col = survival,
group_vars = "groups"
)
# View grouped results
results_grouped$bin_stats
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