---
title: "Trial Analysis"
bibliography: vignettes.bib
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Trial Analysis}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 5
)
```
```{r checkPackages, echo=FALSE}
notFound <- which(
  !sapply(
    c(
      "kableExtra", "tibble", "magrittr", "dplyr",
      "tidyr", "stringr", "kableExtra", "knitr"
    ),
    requireNamespace,
    quietly = TRUE
  )
)
cantRun <- length(notFound) > 0
```
```{r, results="asis", echo=FALSE}
if (cantRun) {
  cat(
    "The following packages are required to run this vignette but are not installed:",
    paste0(names(notFound), collapse = ", "),
    ".  Please install them and try again."
  )
  knitr::knit_exit()
}
```
```{r setup, echo=FALSE}
suppressPackageStartupMessages({
  library(crmPack)
  library(tibble)
  library(magrittr)
  library(dplyr)
  library(tidyr)
  library(stringr)
  library(kableExtra)
})
```
## Introduction
This vignette picks up where the previous one ([Trial Definition](trial_definition.html)), ends.  To recap, our trial defines the six fundamental elements of a CRM trial as 

#### The dose grid
The trial will use a dose grid consisting of the following doses: 1, 3, 9, 20, 30, 45, 60, 80 and 100.  The units in which doses are defined is irrelevant to the operation of the CRM.  

#### The dose-toxicity model
The trial uses a logistic log Normal dose toxicity model

$$ log(\frac{p_i}{1 - p_i}) = \alpha + \beta log(d_i / d^*) $$

where the prior joint distribution of $\alpha$ and $\beta$ is

$$ 
\begin{bmatrix}
\alpha \\
log(\beta)
\end{bmatrix}	\sim
N\begin{pmatrix}
\begin{bmatrix}
-0.85\\0
\end{bmatrix}	,
\begin{bmatrix}
1 & -0.5 \\
-0.5 & 1
\end{bmatrix}	
\end{pmatrix}.
$$

#### The increment rule
The maximum increment for doses greater than `0` and less than `20` is 100 x (1 + 1)%, or 200% of the highest dose used so far, whereas for `20` or more, the maximum increment is 100 x (1 + 0.5)%, or 150% of the highest dose used so far.  

Note that a 2-fold *increment* corresponds to a 3-fold *escalation*.

#### The dose selection rule
Here, we choose to use Neuenschwander's rule [@Neuenschwander2008], in which the dose for the next cohort to be the dose (amongst those doses that are eligible for selection according to the escalation rule) that has the highest posterior chance of having a probability of toxicity in the target range - here [0.2, 0.35) - provided that the dose's chance of having a probability in the overdose range - here [0.35, 1.0] - is less than 0.25.

#### The cohort size
Whilst the dose for the next cohort is 20 or less *and* no DLTs have been observed, the minimum cohort size is 1.  Otherwise, it is 3.

#### The stopping rule
The trial will stop when <i>either</i>

* Twenty patients have been recruited, or.
* Both of the following conditions are true
  * At least three cohorts must have been treated AND
  * The probability that the current estimate of the MTD is in the target toxicity range must be at least 0.5.


#### Trial definition
The code to define these elements of the trial design is given in the [Trial Definition](trial_definition.Rmd) vignette.
```{r, echo=FALSE}
doseGrid <- c(1, 3, 9, 20, 30, 45, 60, 80, 100)
empty_data <- Data(doseGrid = doseGrid)

model <- LogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56
)

my_increments <- IncrementsRelative(
  intervals = c(0, 30),
  increments = c(1, 0.5)
)

my_next_best <- NextBestNCRM(
  target = c(0.2, 0.35),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

my_size <- maxSize(
  CohortSizeRange(intervals = c(0, 30), cohort_size = c(1, 3)),
  CohortSizeDLT(intervals = c(0, 1), cohort_size = c(1, 3))
)

my_stopping <- (StoppingMinCohorts(nCohorts = 3) &
  StoppingTargetProb(target = c(0.2, 0.35), prob = 0.5)) |
  StoppingMinPatients(nPatients = 20)


design <- Design(
  model = model,
  nextBest = my_next_best,
  stopping = my_stopping,
  increments = my_increments,
  cohort_size = my_size,
  data = empty_data,
  startingDose = 3
)
```

## Analysing a trial
Given the trial design constructed above, the process of analysing a real life instance of the trial is simply a matter of providing the model with the actual toxicity status of the participants treated so far.  The escalation rules we defined earlier allow the use of a single patient run-in until either the first DLT is observed or until dose 20 has been administered.  

### The single patient run-in
Assume that the first three patients - dosed at `1`, `3` and `5` - completed the trial without incident, but that the fourth patient - treated at `10` - experienced a DLT.

We provide this information to `crmPack` via a `Data` object:

```{r}
firstFour <- Data(
  x = c(1, 3, 9, 20),
  y = c(0, 0, 0, 1),
  ID = 1:4,
  cohort = 1:4,
  doseGrid = doseGrid
)
```
Within a `Data` object, the doses at which each patient is treated are given by the `x` slot and their toxicity status (a Boolean where a toxicity is represented by a truthy value) by the `y` slot.

The observed data is easily visualised
```{r, fig.alt = "A visual representation of the data from the first four participants.  The first three, treated at doses 1, 3 and 9, do not report any toxicities.  The fourth, treated at 20, does."}
plot(firstFour)
```

and, since the `plot` method returns a `ggplot` object, it is easily customised.

```{r, fig.alt = "The same graph as above, but with a white background to the plot area rather than a grey one."}
plot(firstFour) + theme_light()
```

Now, update the model to obtain the posterior estimate of the dose-toxicity curve:

```{r}
vignetteMcmcOptions <- McmcOptions(burnin = 100, step = 2, samples = 1000)
postSamples <- mcmc(
  data = firstFour,
  model = model,
  options = vignetteMcmcOptions
)
```

The posterior estimate of the dose toxicity curve is easily visualised:

```{r, fig.alt = "A plot of the posterior after the first four participants.  The mean probability of toxicity increases smoothly, with a slight convex curve, from about zero percent at a dose of zero to about 65% at a dose of 100.  The confidence interval extends from 0% to about 25% at a dose of zero and from about 30% to  about 90% at a dose of 100."}
plot(postSamples, model, firstFour)
```

A visual representation of the model's state is obtained with:
```{r, fig.alt = "Two graphs arranged in a single column.  The upper graph shoes green lines of various heights that show the probability each dose is in the target toxicity range.  There is a big arrow pointing to the bar at a dose of 20, indicating tat this dose has the highest probability of being in the target toxicity range.  The lower graph as a similar series of red lines, indicating the probability that each dose is in the overdose range.  There is a horizontal black dashed line at 25%, indicating that this is the highest acceptable probability of being in the overdose range.  The red bars for doses of 30 and above all extend above 25%, indicating that their toxicity is unacceptable.  The toxicity for doses of 20 and below lie below 25%."}
nextBest(
  my_next_best,
  doselimit = 100,
  samples = postSamples,
  model = model,
  data = empty_data
)$plot
```

The lower panel of the plot shows the posterior probability that each dose is in the overdose range.  The dashed horizontal black line shows the acceptable risk of overdose: Doses with red lines which go above this line are considered toxic.  The upper panel shows the probability that each dose is in the target toxicity range.  Clearly, doses of `30` and `45` have the highest probability of being in the target toxicity range.  However, the risk that both are in the overdose range is unacceptable.  Therefore, `20` is the dose recommended for the next cohort.

We can produce a tabulation of the model state with
```{r}
tabulatePosterior <- function(mcmcSamples, observedData) {
  as_tibble(
    nextBest(
      my_next_best,
      doselimit = 100,
      samples = mcmcSamples,
      model = model,
      data = observedData
    )$probs
  ) %>%
    left_join(
      tibble(
        dose = observedData@x,
        WithDLT = observedData@y
      ) %>%
        group_by(dose) %>%
        summarise(
          Treated = n(),
          WithDLT = sum(WithDLT),
          .groups = "drop"
        ),
      by = "dose"
    ) %>%
    replace_na(list(Treated = 0, WithDLT = 0)) %>%
    select(dose, Treated, WithDLT, target, overdose) %>%
    kableExtra::kable(
      col.names = c("Dose", "Treated", "With DLT", "Target range", "Overdose range"),
      digits = c(0, 0, 0, 3, 3)
    ) %>%
    kableExtra::add_header_above(c(" " = 1, "Participants" = 2, "Probability that dose is in " = 2))
}

tabulatePosterior(postSamples, firstFour)
```

From these presentations, we can see that:

  1.  The highest dose so far administered is `20`, so the escalation rule permits doses up to and including `40` to be considered as the dose for the next cohort.  However...
  1.  Doses of `30` and above are considered unsafe
  1.  Of the remaining doses, `20` has the highest posterior probability of being in the target toxicity range
  1.  A DLT has been reported

Items 1 and 4 in the list tell us both that the size of the next cohort should be three.  Items 2 and 3 together imply that the highest dose that can be used in the next cohort is `20`.

Thus, the model's recommendation is that the next cohort should consist of three patients, each treated at `20`.  This can be confirmed programmatically:

```{r}
nextMaxDose <- maxDose(my_increments, firstFour)
nextMaxDose

doseRecommendation <- nextBest(
  my_next_best,
  doselimit = nextMaxDose,
  samples = postSamples,
  model = model,
  data = firstFour
)
doseRecommendation$value
```

However, given that the probability that `20` is in the overdose range is only just less than the threshold of 0.25 (and because the only participant so far treated at `20` experienced a DLT) it would be a perfectly reasonable clinical decision to treat the next cohort at `10` - or, indeed, at any other dose below `20`.  <i>There is absolutely no obligation to follow the CRM dose recommendation without consideration of other factors that might affect the choice of the most appropriate dose for the next cohort.</i>  However, for the purpose of exposition, we will treat the next cohort at `20`, as recommended by the model.

We can confirm that the trial's stopping rules have not been satisfied:

```{r}
stopTrial(
  my_stopping,
  dose = doseRecommendation$value,
  postSamples,
  model,
  firstFour
)
```


### The first full cohort
Assume that none of the three patients in the first full cohort report a DLT:

```{r}
firstFullCohort <- Data(
  x = c(1, 3, 9, 20, 20, 20, 20),
  y = c(0, 0, 0, 1, 0, 0, 0),
  ID = 1:7,
  cohort = c(1:4, rep(5, 3)),
  doseGrid = doseGrid
)
```

Update the model:

```{r}
postSamples1 <- mcmc(
  data = firstFullCohort,
  model = model,
  options = vignetteMcmcOptions
)
```

Tabulate the posterior:

```{r}
tabulatePosterior(postSamples1, firstFullCohort)
```

Should the trial stop?  If not, what dose should be used for the next cohort?

```{r, error=TRUE}
nextMaxDose <- maxDose(my_increments, firstFullCohort)
nextMaxDose

doseRecommendation <- nextBest(
  my_next_best,
  doselimit = nextMaxDose,
  samples = postSamples1,
  model = model,
  data = firstFullCohort
)
doseRecommendation$value

x <- stopTrial(
  my_stopping,
  dose = doseRecommendation$value,
  postSamples1,
  model,
  firstFullCohort
)
attributes(x) <- NULL
x
```

So the trial should continue, treating three patients in the next cohort at `30`.

### The second full cohort
Assume that none of the three patients in the next cohort report a DLT:

```{r}
secondFullCohort <- Data(
  x = c(1, 3, 9, 20, 20, 20, 20, 30, 30, 30),
  y = c(0, 0, 0, 1, 0, 0, 0, 0, 0, 0),
  ID = 1:10,
  cohort = c(1:4, rep(5, 3), rep(6, 3)),
  doseGrid = doseGrid
)
```

Update the model:

```{r}
postSamples2 <- mcmc(
  data = secondFullCohort,
  model = model,
  options = vignetteMcmcOptions
)
```

Tabulate the posterior:

```{r}
tabulatePosterior(postSamples2, secondFullCohort)
```

The dose with the highest posterior probability of being in the target toxicity range is now `45`, but this dose also has an unacceptably high probability of being in the overdose range.  Therefore, the trial should continue and the next cohort should be treated at `30`:
```{r}
nextMaxDose <- maxDose(my_increments, secondFullCohort)
nextMaxDose

doseRecommendation <- nextBest(
  my_next_best,
  doselimit = nextMaxDose,
  samples = postSamples2,
  model = model,
  data = secondFullCohort
)
doseRecommendation$value

x <- stopTrial(
  my_stopping,
  dose = doseRecommendation$value,
  postSamples2,
  model,
  secondFullCohort
)
attributes(x) <- NULL
x
```


### The third full cohort
Assume that none of the three patients in the third cohort report a DLT:

```{r}
thirdFullCohort <- Data(
  x = c(1, 3, 9, rep(20, 4), rep(30, 6)),
  y = c(0, 0, 0, 1, rep(0, 9)),
  ID = 1:13,
  cohort = c(1:4, rep(5, 3), rep(6, 3), rep(7, 3)),
  doseGrid = doseGrid
)
```

Update the model:

```{r}
postSamples3 <- mcmc(
  data = thirdFullCohort,
  model = model,
  options = vignetteMcmcOptions
)
```

Tabulate the posterior:

```{r}
tabulatePosterior(postSamples3, thirdFullCohort)
```

`45` is still the dose with the highest posterior probability of being in the target toxicity range, and its probability of being in the overdose range is now acceptable.  Therefore, the trial should continue and the next cohort should be treated at `45`:
```{r}
nextMaxDose <- maxDose(my_increments, thirdFullCohort)
nextMaxDose

doseRecommendation <- nextBest(
  my_next_best,
  doselimit = nextMaxDose,
  samples = postSamples3,
  model = model,
  data = thirdFullCohort
)
doseRecommendation$value

x <- stopTrial(
  my_stopping,
  dose = doseRecommendation$value,
  postSamples3,
  model,
  thirdFullCohort
)
attributes(x) <- NULL
x
```

### The fourth full cohort
Assume that none of the three patients in the fourth cohort report a DLT:

```{r}
fourthFullCohort <- Data(
  x = c(1, 3, 9, rep(20, 4), rep(30, 6), rep(45, 3)),
  y = c(0, 0, 0, 1, rep(0, 12)),
  ID = 1:16,
  cohort = c(1:4, rep(5:8, each = 3)),
  doseGrid = doseGrid
)
```

Update the model:

```{r}
postSamples4 <- mcmc(
  data = fourthFullCohort,
  model = model,
  options = vignetteMcmcOptions
)
```

Tabulate the posterior:

```{r}
tabulatePosterior(postSamples4, fourthFullCohort)
```

`60` is now the dose with the highest posterior probability of being in the target toxicity range, but its probability of being in the overdose range is unacceptable.  Therefore, the trial should continue and the next cohort should be treated at `45`:
```{r}
nextMaxDose <- maxDose(my_increments, fourthFullCohort)
nextMaxDose

doseRecommendation <- nextBest(
  my_next_best,
  doselimit = nextMaxDose,
  samples = postSamples4,
  model = model,
  data = fourthFullCohort
)
doseRecommendation$value

x <- stopTrial(
  my_stopping,
  dose = doseRecommendation$value,
  postSamples4,
  model,
  fourthFullCohort
)
attributes(x) <- NULL
x
```


### The fifth full cohort
Assume that two of the three patients in the fourth cohort report a DLT:

```{r}
fifthFullCohort <- Data(
  x = c(1, 3, 9, rep(20, 4), rep(30, 6), rep(45, 6)),
  y = c(0, 0, 0, 1, rep(0, 13), 1, 1),
  ID = 1:19,
  cohort = c(1:4, rep(5:9, each = 3)),
  doseGrid = doseGrid
)
```

Update the model:

```{r}
postSamples5 <- mcmc(
  data = fifthFullCohort,
  model = model,
  options = vignetteMcmcOptions
)
```

Tabulate the posterior:

```{r}
tabulatePosterior(postSamples5, fifthFullCohort)
```

`45` remains the dose with the highest posterior probability of being in the target toxicity range, and its probability of being in the overdose range is acceptable.  Moreover, the probability that `45` is in the target toxicity range is above 0.5 and more than three cohorts have been treated in total.  Therefore, the trial should stop and conclude that `45` is the MTD:
```{r}
nextMaxDose <- maxDose(my_increments, fifthFullCohort)
nextMaxDose

doseRecommendation <- nextBest(
  my_next_best,
  doselimit = nextMaxDose,
  samples = postSamples5,
  model = model,
  data = fifthFullCohort
)
doseRecommendation$value

x <- stopTrial(
  my_stopping,
  dose = doseRecommendation$value,
  postSamples5,
  model,
  fifthFullCohort
)
x
```

##  Summarising the trial results

crmPack provides a wealth of information about the trial's results.  The following code snippets illustrate some of the many possibilities for how the trial might be summarised.

```{r, fig.alt = "A visual representation of the data after nineteen participants have been treated.  One each at doses 1, 3 and 9; four at a dose of 20; 6 at a dose of 30 and 6 at a dose of 45.  Toxicitiues were reported by participants 4 (at a dose of 20) and 18 and 19 (both at a dose of 45)."}
plot(fifthFullCohort)
```

```{rfig.alt = "A plot of the posterior after nineteen participants have been treated.  The mean probability of toxicity increases smoothly from about zero percent at a dose of zero to about 55% at a dose of 100.  The confidence interval extends from 0% to about 6% at a dose of zero and from about 22% to  about 90% at a dose of 100."}
plot(postSamples5, model, fifthFullCohort)
```

```{rfig.alt = "Two graphs arranged in a single column.  The upper graph shoes green lines of various heights that show the probability each dose is in the target toxicity range.  There is a big arrow pointing to the bar at a dose of 45, indicating that this dose has the highest probability of being in the target toxicity range.  The lower graph as a similar series of red lines, indicating the probability that each dose is in the overdose range.  There is a horizontal black dashed line at 25%, indicating that this is the highest acceptable probability of being in the overdose range.  The red bars for doses of 60 and above all extend above 25%, indicating that their toxicity is unacceptable.  The toxicity for doses of 45 and below lie below 25%."}
doseRecommendation$plot
```

With a little bit of work, we can obtain a more detailed summary and plot of the posterior probabilities of toxicity at each dose:
```{r, fig.alt = "A graph showing the posterior density of of the probability of toxicity for all doses greater than nine.  The mode of each density moves to the right as dose increases.  The densities for low doses are heaviliy skewed to the left.  Densities for higher doses are more symmetric and flatter."}
slotNames(model)

fullSamples <- tibble(
  Alpha = postSamples5@data$alpha0,
  Beta = postSamples5@data$alpha1
) %>%
  expand(nesting(Alpha, Beta), Dose = doseGrid) %>%
  rowwise() %>%
  mutate(P = probFunction(model, alpha0 = Alpha, alpha1 = Beta)(dose = Dose)) %>%
  ungroup()

fullSummary <- fullSamples %>%
  group_by(Dose) %>%
  summarise(
    Mean = mean(P),
    Median = median(P),
    Q = list(quantile(P, probs = c(0.05, 0.1, 0.25, 0.75, 0.9, 0.95), na.rm = TRUE))
  ) %>%
  unnest_wider(
    col = Q,
    names_repair = function(.x) {
      ifelse(
        str_detect(.x, "\\d+%"),
        sprintf("Q%02.0f", as.numeric(str_remove_all(.x, "%"))),
        .x
      )
    }
  )

fullSummary %>%
  kableExtra::kable(
    col.names = c("Dose", "Mean", "Median", "5th", "10th", "25th", "75th", "90th", "95th"),
    digits = c(0, rep(3, 8))
  ) %>%
  add_header_above(c(" " = 3, "Quantiles" = 6)) %>%
  add_header_above(c(" " = 1, "P(Toxicity)" = 8))

fullSamples %>%
  filter(Dose > 9) %>%
  ggplot() +
  geom_density(aes(x = P, color = as.factor(Dose))) +
  theme_light() +
  theme(
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    axis.ticks.y = element_blank()
  ) +
  labs(
    title = "Posterior PDFs for doses > 9",
    colour = "Dose"
  )
```

```{r, fig.alt = "A visual representation of the posterior dose - toxicity curve.  Very closely spaced solid lines in black and blue, representing the mean and median estimate of toxicity for each dose rise almost linearly from zero percent for a dose of zero to about 55% for a dose of 100.  Shading extends to each side of the two solid lines.  The transparency of the shading increases with distance from the solid lines.  The shading is funnel shaped, with a narrow mneck at a dose of 100 and a wider mouth at a dose of 100.  The shading represents the central 90%, 80% and 50% confidence intervals for the posterior mean estimate of toxicity at each dose."}
fullSummary %>%
  ggplot(aes(x = Dose)) +
  geom_ribbon(aes(ymin = Q05, ymax = Q95), fill = "steelblue", alpha = 0.25) +
  geom_ribbon(aes(ymin = Q10, ymax = Q90), fill = "steelblue", alpha = 0.25) +
  geom_ribbon(aes(ymin = Q25, ymax = Q75), fill = "steelblue", alpha = 0.25) +
  geom_line(aes(y = Mean), colour = "black") +
  geom_line(aes(y = Median), colour = "blue") +
  theme_light() +
  labs(
    title = "Posterior Dose toxicity curve",
    colour = "Dose",
    y = "P(Toxicity)"
  )
```

## Note
The analyses presented in this vignette have used chains of a very short length.  This is purely for convenience.  Analyses of real trials should use considerably longer chains.  As an example, an effective sample size of approximately 40,000 is required to estimate a percentage to within &plusmn;1%.

## References
