Calculate Metrics for Trauma System Performance
Check if Elements Are Not in a Vector
Impute Numeric Column Values
Exploratory Data Analysis, Normality Testing, and Visualization
Create Nonlinear Probability of Survival Bins
Normalize a Numeric Vector
Convert Numbers into Readable Abbreviated Formats
Format Numeric Variables as Percentages
Calculate Probability of Survival Using TRISS Method
Bin-Level Summary for Relative Mortality Metric (RMM)
Relative Mortality Metric (RMM) Calculation
Get Season Based on a Date
SEQIC Indicator 1 – Trauma Team Response Evaluation
SEQIC Indicator 10 – Trauma Team Activation Appropriateness
SEQIC Indicator 11 – Overtriage for Minor Trauma Patients
SEQIC Indicator 12 - Timeliness of Data Entry Post-Discharge
SEQIC Indicator 13 – Validation of Trauma Registry Records
SEQIC Indicator 2 – Missing Incident Time
SEQIC Indicator 3 - Presence of Probability of Survival Calculations
SEQIC Indicator 4 - Autopsy and Long LOS Without Autopsy
SEQIC Indicator 5 - Alcohol and Drug Screening
SEQIC Indicator 6 - Delayed Arrival Following Low GCS
SEQIC Indicator 7 - Delayed Arrival to Definitive Care
SEQIC Indicator 8 - Survival by Risk Group
SEQIC Indicator 9 - Emergency Department Transfer Timeliness
Label Small Counts Based on a Cutoff
Assign Significance Codes Based on P-Values
Customizable Minimalistic ggplot2 Theme
View the Current Patient Population Case Mix Compared to the Major Tra...
Calculate Trauma Hospital Performance Based on Robust and Validated Me...
traumar: Calculate Metrics for Trauma System Performance
Validate a Character or Factor Input
Validate Choice
Validate Class
Validate Complete Input
Validate Data Extraction
Validate Data Structure
Validate Error Type
Validate Length of an Input
Validate Column Names
Validate Numeric Input
Validate Set Equality
Classify Dates as Weekday or Weekend
Hospitals, hospital systems, and even trauma systems that provide care to injured patients may not be aware of robust metrics that can help gauge the efficacy of their programs in saving the lives of injured patients. 'traumar' provides robust functions driven by the academic literature to automate the calculation of relevant metrics to individuals desiring to measure the performance of their trauma center or even a trauma system. 'traumar' also provides some helper functions for the data analysis journey. Users can refer to the following publications for descriptions of the methods used in 'traumar'. TRISS methodology, including probability of survival, and the W, M, and Z Scores - Flora (1978) <doi:10.1097/00005373-197810000-00003>, Boyd et al. (1987, PMID:3106646), Llullaku et al. (2009) <doi:10.1186/1749-7922-4-2>, Singh et al. (2011) <doi:10.4103/0974-2700.86626>, Baker et al. (1974, PMID:4814394), and Champion et al. (1989) <doi:10.1097/00005373-198905000-00017>. For the Relative Mortality Metric, see Napoli et al. (2017) <doi:10.1080/24725579.2017.1325948>, Schroeder et al. (2019) <doi:10.1080/10903127.2018.1489021>, and Kassar et al. (2016) <doi:10.1177/00031348221093563>. For more information about methods to calculate over- and under-triage in trauma hospital populations and samples, please see the following publications - Peng & Xiang (2016) <doi:10.1016/j.ajem.2016.08.061>, Beam et al. (2022) <doi:10.23937/2474-3674/1510136>, Roden-Foreman et al. (2017) <doi:10.1097/JTN.0000000000000283>.
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