
## 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

![](man/figures/calibration_plot.png)

### Risk stratification

![](man/figures/risk_group_plot.png)

### Dynamic BEWRS scoring

![](man/figures/dynamic_bewrs_scatter.png) \## 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>
