---
title: "Customizing RBesT plots"
author: "Baldur Magnusson"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Customizing RBesT plots}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
---

# Introduction

This vignette demonstrates how to work with the forest plot provided
by the `RBesT` package. We show how to modify the default plot and, for
advanced users, how to extract data from a ggplot object to create a
new plot from scratch. Finally we recreate plots from a case study
presented in the training materials.

For more general information on plotting in R, we recommend the
following resources:

- [bayesplot](https://CRAN.R-project.org/package=bayesplot) - library on which the `RBesT` plotting functionality is built
- [ggplot2](https://ggplot2.tidyverse.org/reference/) - powerful library for graphics in R
- [R Cookbook - Graphs](http://www.cookbook-r.com/Graphs/) - general reference for R and graphics in R

```{r setup, include=FALSE}
##suppressPackageStartupMessages(library(tidyverse))
library(knitr)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE, fig.width = 1.62*4, fig.height = 4)
## setup up fast sampling when run on CRAN
if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
    options(RBesT.MC.warmup=250, RBesT.MC.iter=500, RBesT.MC.chains=2)
}
```

```{r}
# Load required libraries
library(RBesT)
library(ggplot2)
library(dplyr)
library(tidyr)
library(bayesplot)

# Default settings for bayesplot
color_scheme_set("blue")
theme_set(theme_default(base_size=12))

# Load example gMAP object
set.seed(546346)
map_crohn <- gMAP(cbind(y, y.se) ~ 1 | study,
                  family=gaussian,
                  data=transform(crohn, y.se=88/sqrt(n)),
                  weights=n,
                  tau.dist="HalfNormal", tau.prior=44,
                  beta.prior=cbind(0,88))

print(map_crohn)
```

# Forest plot

The default forest plot is a "standard" forest plot with the
Meta-Analytic-Predictive (MAP) prior additionally summarized in the
bottom row:

```{r}
forest_plot(map_crohn)
```

We can also include the model-based estimates for each study, and add
a legend to explain the different linetypes.

```{r}
forest_plot(map_crohn, model="both") + legend_move("right")
```

We can modify the color scheme as follows (refer to
`help(color_scheme_set)` for a full list of themes):

```{r}
# preview a color scheme
color_scheme_view("mix-blue-red")
# and now let's use it
color_scheme_set("mix-blue-red") 
forest_plot(map_crohn)
color_scheme_set("gray")
forest_plot(map_crohn)
```

The point size can be modified and the vertical line removed:

```{r}
color_scheme_set("blue")
forest_plot(map_crohn, size=0.5, alpha=0)
```

## Presentation-ready plots

If a plot is to be used for a presentation or in a document such as
the study protocol, it is recommended to use sufficiently large font
sizes (e.g. about as large as the fonts on the same slide or in the
same document) and that elements of the plot are clearly 
visible. Here we show a few simple statements that can be used for
this purpose.

```{r, fig.width=10, fig.height=6}
# adjust the base font size
theme_set(theme_default(base_size=16))
forest_plot(map_crohn, model="both", est="MAP", size=1) + legend_move("right") +
  labs(title="Forest plot", subtitle="Results of Meta-Analytic-Predictive (MAP) analysis", 
       caption="Plot shows point estimates (posterior medians) with 95% intervals")
```

We also recommend saving plots explicitly with the `ggsave` function,
which allows control (and hence consistency) of image size. Note that
the font size will be enforced in the requested size; a small image
with large font size may result in too little space for the plot
itself. The image is sized according to the golden cut
($\phi=\frac{1+\sqrt5}{2} \approx 1.62$) which is perceived as a
pleasing axis ratio. Png is the recommended image file type for 
presentations and study documents. 

```{r, eval=FALSE}
ggsave("plot1.png", last_plot(), width=1.62*2.78, height=2.78, unit="in") # too small for the chosen font size
ggsave("plot2.png", last_plot(), width=1.62*5.56, height=5.56, unit="in") # fits a single ppt slide quite well
```

# Advanced topics

## Extract data from a `ggplot` object

In some situations, desired modifications to a plot provided by
`RBesT` may not be possible given the returned `ggplot` object. If a
truly tailored plot is desired, the user must extract the data from
this object and create a graph from scratch using `ggplot`
functions. Recall the original forest plot:

```{r, echo=FALSE}
theme_set(theme_default(base_size=12))
forest_plot(map_crohn) + labs(title="Original forest plot")
```

Suppose we wish to use different symbols for the meta/stratified point
estimates and a different linestyle for the vertical line. A tailored
plot can be created as follows.

```{r}
# Extract the data from the returned object
fp_data <- forest_plot(map_crohn)$data
print(fp_data, digits=2)
# Use a two-component map mixture to compute the vertical line location
map_mix <- mixfit(map_crohn, Nc=2)
# Finally compose a ggplot call for the desired graph
ggplot(fp_data, aes(x=study, y=median, ymin=low, ymax=up, linetype=model, shape=model)) + 
  geom_pointrange(size=0.7, position=position_dodge(width=0.5)) + 
  geom_hline(yintercept=qmix(map_mix, 0.5), linetype=3, alpha=0.5) +
  coord_flip() + theme_bw(base_size=12) + theme(legend.position="None") + 
  labs(x="", y="Response", title="Modified forest plot")
```

## Design plots a clinical trial

Here we show how to create outcome and operating characteristic plots 
for a clinical trial comparing a developmental drug against placebo. 
The primary endpoint is binary (with event probability $p$) and we use
an informative prior for the placebo arm event rate. The experimental 
drug is believed to lower the event rate, and the criteria for study 
outcome are hence as follows: 

$$
\begin{align*}
\textrm{Criterion 1:}  \Pr(p_{trt} / p_{pbo} < 1) &> 0.9\\
\textrm{Criterion 2:}  \Pr(p_{trt} / p_{pbo} < 0.5) &> 0.5
\end{align*}
$$

The outcome is success (GO) if both criteria are satisfied, futility
(STOP) if neither is satisfied, and indeterminate if only one or the
other is satisfied.

We now create a plot that shows the study conclusion, given any
combination of outcomes on the two treatment arms.

```{r}
# Define prior distributions
prior_pbo <- mixbeta(inf1=c(0.60, 19, 29), inf2=c(0.30, 4, 5), rob=c(0.10, 1, 1))
prior_trt <- mixbeta(c(1, 1/3, 1/3))

# Study sample size
n_trt <- 50
n_pbo <- 20

# Create decision rules and designs to represent success and futility
success <- decision2S(pc=c(0.90, 0.50), qc=c(log(1), log(0.50)), lower.tail=TRUE, link="log")
futility <- decision2S(pc=c(0.10, 0.50), qc=c(log(1), log(0.50)), lower.tail=FALSE, link="log")
design_suc <- oc2S(prior_trt, prior_pbo, n_trt, n_pbo, success)
design_fut <- oc2S(prior_trt, prior_pbo, n_trt, n_pbo, futility)
crit_suc <- decision2S_boundary(prior_trt, prior_pbo, n_trt, n_pbo, success)
crit_fut <- decision2S_boundary(prior_trt, prior_pbo, n_trt, n_pbo, futility)

# Create a data frame that holds the outcomes for y1 (treatment) that define success and futility, 
# conditional on the number of events on y2 (placebo)
outcomes <- data.frame(y2=c(0:n_pbo), suc=crit_suc(0:n_pbo), fut=crit_fut(0:n_pbo), max=n_trt)
outcomes$suc <- with(outcomes, ifelse(suc<0, 0, suc)) # don't allow negative number of events

# Finally put it all together in a plot. 
o <- 0.5 # offset
ggplot(outcomes, aes(x=y2, ymin=-o, ymax=suc+o)) + geom_linerange(size=4, colour="green", alpha=0.6) + 
  geom_linerange(aes(ymin=suc+o, ymax=fut+o), colour="orange", size=4, alpha=0.6) + 
  geom_linerange(aes(ymin=fut+o, ymax=max+o), colour="red", size=4, alpha=0.6) + 
  annotate("text", x=c(2,14), y=c(36,8), label=c("STOP", "GO"), size=10) + 
  scale_x_continuous(breaks=seq(0,n_pbo,by=2)) + 
  scale_y_continuous(breaks=seq(0,n_trt,by=4)) + 
  labs(x="Events on placebo", y="Events on treatment", title="Study outcomes") + 
  coord_flip() + theme_bw(base_size=12)
```

We can also use the design functions that were already derived
(`design_suc` and `design_fut`) to compute operating characteristics.

```{r, fig.width=8.5, fig.height=5.5}
# Define the grid of true event rates for which to evaluate OC
p_trt <- seq(0, 0.5, length=200)
p_pbo <- c(0.35, 0.40, 0.45, 0.50)

# Loop through the values for placebo and compute outcome probabilities 
oc_list <- lapply(p_pbo, function(x) {
  p_go <- design_suc(p_trt, x)
  p_stop <- design_fut(p_trt, x)
  data.frame(p_trt, p_pbo=x, Go=p_go, Stop=p_stop, Indeterminate=1-p_go-p_stop)
})
# The above returns a list, so we bind the elements together into one data frame
oc <- bind_rows(oc_list) 
# And convert from wide to long format
oc <- gather(oc, "Outcome", "Probability", 3:5)
oc$facet_text <- as.factor(paste("True placebo rate = ", oc$p_pbo, sep=""))

# Finally we are ready to plot
ggplot(oc, aes(x=p_trt, y=Probability, colour=Outcome, linetype=Outcome)) + 
  facet_wrap(~facet_text) + geom_line(size=1) + 
  scale_colour_manual(values=c("green", "orange", "red"), name="Outcome") + 
  scale_linetype(guide=FALSE) + 
  geom_hline(yintercept=c(0.1, 0.8), linetype=3) + 
  scale_y_continuous(breaks=seq(0, 1, by=0.2)) + 
  labs(x="True event rate for treatment", y="Probability", title="Operating Characteristics") + 
  theme_bw(base_size=12) 
```

```{r}
sessionInfo()
```
