traumar::seqic_indicator_7() in
the portion of the code where the denominator_7 is
calculated. The denominator is no longer calculated as
dplyr::n(), and instead uses the following in order to
accurately calculate the denominator that reflects the definitive care
population:denominator_7 = sum({{ transfer_out_indicator }}} %in% c(FALSE, "No"), na.rm = TRUE)
Added the functions to perform all data validation. These
functions take the form of validate_*(), such as
validate_character_factor(). After exploring options from
other packages to lean on existing methods to validate data via
functional programming, the decision was made to create functions
“in-house” to avoid issues with adding another dependency. The
validate_*() family of functions is not exported from
traumar.
Updated unit tests throughout the package given the addition of new data validation methodology.
Ensured that plenty of comments are provided throughout the functions in the package to enhance understanding of the code and collaboration.
pretty_number() now uses a digits
argument instead of n_decimal. n_decimal has
been deprecated and will now issue a warning, but the function will
still work using the n_decimal argument. In a later
release, n_decimal will be fully deprecated.
rmm() and rm_bin_summary() will now
fail if the seed argument is not of class numeric or
integer.
Added section headers throughout the code base to make navigation within most modern IDEs easier.
Error/warning messages will now directly name the function in the
call stack that is most helpful. For example, if some data validation
triggers an error within traumar::seqic_indicator1(), then
traumar::seqic_indicator1() will show up in the error
message, instead of some other function such as a cli
function or one of the new validate_*() functions.
Improved small_count_label() code readability in the
control flow chunk where values less than the cutoff are manipulated
depending on user input.
trauma_performance() now acts as a wrapper for
trauma_case_mix() as trauma_performance() now
calls trauma_case_mix() under the hood instead of
explicitly performing the same operations. This makes the functionality
more maintainable for any future iterations.
probability_of_survival() was updated to enhance
code readability and leverage mathematical notation in the calculation
of predicted survival probabilities. The function now aligns with the
coefficients published in Norouzi et al. (2013) and Merchant et
al. (2023). Consistent with Boyd et al. (1987), the function does not
treat patients under 15 years of age differently and accounts for
penetrating injuries similarly to other age groups. This update ensures
a standardized approach to calculating survival probabilities for both
blunt and penetrating traumas.
From this release forward, all development of
traumar will be done in the IDE Positron!
rmm(), rm_bin_summary(), and
nonlinear_bins(): Updated the documentation for the
threshold arguments along with the divisor arguments. These were updated
along with the Details section of the nonlinear_bins()
function documentation to provide a better explanation of the binning
algorithm under the hood.
probability_of_survival(): Updated the Return
section documentation to be more accurate that the output is not a
percentage, it is the predicted probability of survival on a scale from
0 to 1. A previous version of this function was multiplied by 100 to
seem more like a percentage.
Within the trauma_performance() function, renamed
the variable predicted_prob_death to
scale_factor which is commensurate with the source
literature.
updated comments in trauma_performance() for
z_method method of the Z_score to reflect the
right text.
In trauma_performance(), completed the comment where
the scale_factor is created so that it is complete and
clear.
Corrected a test error at CRAN from using bootstrap CI process in
testing with 100,000 observations and 100 bootstrap samples to make sure
rmm() and rm_bin_summary() ran in under 60
sec. That test now does not use the bootstrap process so the core
function can be tested and will always run in under a minute with
100,000 observations.
Cleaned up other tests within for relative_mortality.R that were checking for correct error / warning handling where multiple lines of output were sent to the console. Built a custom function to deal with those scenarios and correctly perform those unit tests.
This minor release introduces functionality to demonstrate how the Iowa trauma system currently engages in the quality improvement process using the System Evaluation and Quality Improvement Committee (SEQIC) Indicators.
Added a Contributor Code of Conduct and support information.
Additionally, a convenience function is_it_normal()
provides the ability for users of traumar to get
descriptive statistics on one or more numeric variables, with optional
normality tests, and diagnostic plots (for one variable only). Grouping
functionality is also supported in is_it_normal() to
conduct exploratory data analysis of one or more variables within zero
or more groups.
Added the following functions:
seqic_indicator_1()seqic_indicator_2()seqic_indicator_3()seqic_indicator_4()seqic_indicator_5()seqic_indicator_6()seqic_indicator_7()seqic_indicator_8()seqic_indicator_9()seqic_indicator_10()seqic_indicator_11()seqic_indicator_12()seqic_indicator_13()is_it_normal()A fix was applied to nonlinear_bins() to make the
percent column calculate correctly when groups were not
introduced.
Removed hard-coded rounding from most calculations within the package where possible.
Improved examples for the the package’s README,
probability_of_survival(), nonlinear_bins(),
rmm(), and rm_bin_summary() using more helpful
data.
Improved error messages coming from nonlinear_bins()
when the argument Ps_col does not follow the expected
distribution of the calculated probability of survival, and/or a sample
size too small to calculate bins is passed to the function, including
when passed to rmm() and
rm_bin_summary().
Code formatting changed to the air package through
the RStudio IDE.
Updated data validation for trauma_case_mix(),
trauma_performance(), nonlinear_bins(),
rmm(), and rm_bin_summary() to provide
improved messaging related to missings in Ps_col and
outcome_col .
Across functions using the probability of survival calculation, it is expected that Ps values have a range of [0, 1]. Functions will no longer handle values in percentage format (e.g. 10, 50, 98).
The outcome argument was removed from
trauma_performance() to remove ambiguity in the nature of
the outcome_col values. Only values of
TRUE/FALSE and 1/0 are accepted.
The diagnostics argument was removed from
trauma_performance() to make the user interface smoother.
Instead of providing guidance via outputs to the console, users are
encouraged to seek assistance with interpreting results via the source
academic literature and the package documentation.
trauma_performance() will no longer provide a
pivoted output as a default. Users can elect to pivot the outputs as
needed in their workflows.
rmm() and rm_bin_summary() now have a
new argument bootstrap_ci that allows a user to elect to
use the bootstrap CIs, or not. bootstrap_ci defaults to
TRUE in order to better support backward
compatibility.
Added optional grouping functionality to
nonlinear_bins(), rmm(), and
rm_bin_summary().
Setting group_vars = NULL applies the functions to
the entire dataset without subgrouping.
For pivoting the rmm() outputs longer, setting
pivot = TRUE will work when group_vars is
invoked by pivoting longer with the grouping context.
Improved NA handling in rmm() and
rm_bin_summary().
Ensured RMM calculations remain within the expected range of [-1 to 1], including their 95% confidence intervals.
Optimized nonlinear_bins() by replacing its internal
for loop with dplyr functions, enhancing
accuracy and efficiency without introducing breaking changes.
Improved command line messaging and documentation within
rmm() and rm_bin_summary() regarding
probability of survival values Ps_col < 0 and
Ps_col > 1. Now, these functions will throw an error if
probability of survival values are Ps_col < 0 or
Ps_col > 1.
The nonlinear_bins() function has improved data
validation for the Ps_col variable.
probability_of_survival() function.rmm()rm_bin_summary()nonlinear_bins()trauma_case_mix()trauma_performance()rmm()rm_bin_summary()nonlinear_bins()impute()normalize()season()weekend()pretty_number()pretty_percent()small_count_label()stat_sig()theme_cleaner()%not_in%