Overview
bewrs provides tools for Bayesian early-warning risk surveillance,
dynamic risk scoring, validation, calibration assessment,
decision-theoretic intervention optimisation, and Expected Value of
Intervention analysis for healthcare performance monitoring.
Example outputs
Calibration assessment
Risk stratification
Dynamic BEWRS scoring
## Methodological references
The methods implemented in bewrs are motivated by work on Bayesian
hierarchical modelling, healthcare provider profiling, prediction model
validation, calibration assessment, decision curve analysis, and
Bayesian decision theory, including:
- Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB.
Bayesian Data Analysis. 3rd ed. CRC Press; 2013.
- Spiegelhalter DJ. Funnel plots for comparing institutional
performance. BMJ. 2005;331:302–305. doi:10.1136/bmj.331.7512.302
- Vickers AJ, Elkin EB. Decision curve analysis. Medical Decision
Making. 2006;26(6):565–574. doi:10.1177/0272989X06295361
- Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance
of prediction models. Epidemiology. 2010;21(1):128–138.
doi:10.1097/EDE.0b013e3181c30fb2
- Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW.
Calibration: the Achilles heel of predictive analytics. BMC
Medicine. 2019;17:230. doi:10.1186/s12916-019-1466-7