---
title: "Colonoscopy follow-up measure example"
author: "Kenneth Nieser"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Colonoscopy follow-up measure example}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 8,
  fig.height = 6
)
```

This vignette includes an example of hospital profiling based on a measure of whether patients receive appropriate recommendations for a follow-up colonoscopy.

```{r setup}
library(QualityMeasure)
```


-------------------------------------------------------------

First, we'll load the dataset included with the `QualityMeasure` package.

```{r}
df <- colonoscopy
knitr::kable(head(df), 'simple')
```

Next, we will calculate reliability using the Beta-Binomial method for aggregated data.

```{r}
BB.results <- calcBetaBin(df = df, df.aggregate = T, n = 'n', x = 'x')
```

Beta-Binomial parameter estimates are: alpha = `r round(BB.results$alpha,3)` and beta = `r round(BB.results$beta, 3)`. The between-entity variance in rates is `r round(BB.results$var.b, 3)`.

Below is a summary of the distribution of entity-level reliability estimates.

```{r}
summary(BB.results$est.BB)
```
We can also plot entity-level reliability results by sample size.

```{r}
plot.df <- data.frame(
  n = df$n,
  rel = BB.results$est.BB
)

fig <- ggplot(data = plot.df, aes(x = n, y = rel)) +
  geom_point(size = 3) +
  geom_hline(yintercept = median(BB.results$est.BB), linetype = 'dashed', col = 'red', linewidth = 2) +
  annotate('text', x = 1700, y = 0.92, label = 'Median reliability', size = 6, col = 'red') +
  xlab('Entity sample size') +
  ylab('Reliability') +
  theme_classic() +
  theme(
    panel.grid.major = element_line(),
    panel.grid.minor = element_line(),
    axis.text = element_text(size = 16),
    axis.ticks.length = unit(.25, 'cm'),
    axis.title = element_text(size = 18, face = 'bold')
  )
fig
```