A205-CRM.Rmd

---
title: "Continual Reassessment Method"
output: 
  rmarkdown::html_vignette:
    df_print: tibble
bibliography: library.bib
vignette: >
  %\VignetteIndexEntry{Continual Reassessment Method}
  %\VignetteEngine{knitr::rmarkdown}
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---

The `escalation` package by Kristian Brock.
Documentation is hosted at https://brockk.github.io/escalation/

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7, fig.height = 5
)

options(rmarkdown.html_vignette.check_title = FALSE)
```

# Introduction
The continual reassessment method (CRM) was introduced by @OQuigley1990.
It has proved to be a truly seminal dose-finding design, spurring many revisions, variants and imitations.

# Summary of the CRM Design
Pinning a finger on what is _the_ CRM design is complicated because there have been so many versions over the years.

At its simplest, the CRM is a dose-escalation design that seeks a dose with probability of toxicity closest to some pre-specified target toxicity rate, $p_T$, in an homogeneous patient group.
The hallmark that unifies the CRM variants is the assumption that the probability of toxicity, $p_i$, at the $i$th dose, $x_i$, can be modelled using a smooth mathematical function:

$$ p_i = F(x_i, \theta), $$

where $\theta$ is a general vector of parameters.
Priors are specified on $\theta$, and the dose with posterior estimate of $p_i$ closest to $p_T$ is iteratively recommended to the next patient(s).

Different variants of the CRM use different forms for $F$.
We consider those briefly now.

### Hyperbolic tangent model
@OQuigley1990 first introduced the method using

$$ p_i = \left( \frac{\tanh{(x_i)} + 1}{2} \right)^\beta, $$

with an exponential prior was placed on $\beta$.


### Empiric model (aka power model)

$$ p_i = x_i^{\exp(\beta)}, $$

for dose $x_i \in (0, 1)$;

### One-parameter logistic model

$$ \text{logit} p_i = a_0 + \exp{(\beta)} x_i, $$

where $a_0$ is a pre-specified constant, and $x_i \in \mathbb{R}$.

### Two-parameter logistic model

$$ \text{logit} p_i = \alpha + \exp{(\beta)} x_i, $$
for $x_i \in \mathbb{R}$.

Other model considerations include:

### Priors
In each of these models, different distributions may be used for the parameter priors.

### Toxicity skeletons and standardised doses
The $x_i$ dose variables in the models above do not reflect the raw dose quantities given to patients.
For example, if the dose is 10mg, we do not use `x=10`.
Instead, a _skeleton_ containing estimates of the probabilities of toxicity at the doses is identified.
This skeleton could reflect the investigators' prior expectations of toxicities at all of the doses; or it could reflect their expectations for some of the doses with the others interpolated in some plausible way.
The $x_i$ are then calculated so that the model-estimated probabilities of toxicity with the parameters taking their prior mean values match the skeleton.
This will be much clearer with an example.

In a five dose setting, let the skeleton be $\pi = (0.05, 0.1, 0.2, 0.4, 0.7)$.
That is, the investigators believe before the trial commences that the probbaility of toxicity at the second dose is 10%, and so on.
Let us assume that we are using a one-parameter logistic model with $a_0 = 3$ and $\beta ~ \sim N(0, 1)$.
Then we require that 

$$ \text{logit} \pi_i = 3 + e^0 x_i = 3 + x_i, $$

i.e.

$$ x_i = \text{logit} \pi_i - 3. $$

This yields the vector of standardised doses $x = (-5.94, -5.20, -4.39, -3.41, -2.15)$.
Equivalent transformations can be derived for the other model forms.
The $x_i$ are then used as covariates in the model-fitting.
CRM users specify their skeleton, $\pi$, and their parameter priors.
From these, the software calculates the $x_i$.
The actual doses given to patients in SI units do not actually feature in the model.


# Implementation in `escalation`

`escalation` simply aims to give a common interface to dose-selection models to facilitate a grammar for specifying dose-finding trial designs.
Where possible, it delegates the mathematical model-fitting to existing R packages.
There are several R packages that implement CRM models.
The two used in `escalation` are the `dfcrm` package [@Cheung2011; @dfcrm]; and the `trialr` package [@Brock2019; @trialr].
They have different strengths and weaknesses, so are suitable to different scenarios.
We discuss those now.

### dfcrm 
[`dfcrm`](https://cran.r-project.org/package=dfcrm) offers:

* the **empiric** model with normal prior on $\beta$;
* the **one-parameter logistic** model with normal prior on $\beta$.

`dfcrm` models are fit in `escalation` using the `get_dfcrm` function.
Examples are given below.

### trialr
[`trialr`](https://cran.r-project.org/package=trialr) offers:

* the **empiric** model with normal prior on $\beta$;
* the **one-parameter logistic** model with normal prior on $\beta$;
* the **one-parameter logistic** model with gamma prior on $\beta$;
* the **two-parameter logistic** model with normal priors on $\alpha$ and $\beta$.

`trialr` models are fit in `escalation` using the `get_trialr_crm` function.

Let us commence by replicating an example from p.21 of @Cheung2011.
They choose the following parameters:

```{r}
skeleton <- c(0.05, 0.12, 0.25, 0.40, 0.55)
target <- 0.25
a0 <- 3
beta_sd <- sqrt(1.34)
```

Let us define a model fitter using the `dfcrm` package:

```{r, message=FALSE}
library(escalation)
model1 <- get_dfcrm(skeleton = skeleton, target = target, model = 'logistic', 
                    intcpt = a0, scale = beta_sd)
```

and a fitter using the `trialr` package:
```{r}
model2 <- get_trialr_crm(skeleton = skeleton, target = target, model = 'logistic', 
                         a0 = a0, beta_mean = 0, beta_sd = beta_sd)
```

Names for the function parameters `skeleton`, `target`, and `model` are standardised by `escalation` because they are fundamental.
Further parameters (i.e. those in the second lines of each of the above examples) are passed onwards to the model-fitting functions in `dfcrm` and `trialr`.
You can see that some of these parameter names vary between the approaches.
E.g., what `dfcrm` calls the `intcpt`, `trialr` calls `a0`.
Refer to the documentation of the `crm` function in `dfcrm` and `stan_crm` in `trialr` for further information.

We then fit those models to the notional outcomes described in the source text:
```{r}
outcomes <- '3N 5N 5T 3N 4N'

fit1 <- model1 %>% fit(outcomes)
fit2 <- model2 %>% fit(outcomes)
```


The dose recommended by each of the models for the next patient is:

```{r}
fit1 %>% recommended_dose()
fit2 %>% recommended_dose()
```

Thankfully, the models agree.
They advocate staying at dose 4, wary of the toxicity already seen at dose 5.

If we take a summary of each model fit:
```{r}
fit1 %>% summary()
```

```{r}
fit2 %>% summary()
```

We can see that they closely agree on model estimates of the probability of toxicity at each dose.
Note that the median perfectly matches the mean in the `dfcrm` fit because it assumes a normal posterior distribution on $\beta$.
In contrast, the `trialr` class uses `Stan` to fit the model using Hamiltonian Monte Carlo sampling.
The posterior distributions for the probabilities of toxicity are evidently non-normal and positively-skewed because the median estimates are less than the mean estimates.

Let us imagine instead that we want to fit the empiric model.
That simply requires we change the `model` variable and adjust the prior parameters:

```{r}
model3 <- get_dfcrm(skeleton = skeleton, target = target, model = 'empiric', 
                    scale = beta_sd)

model4 <- get_trialr_crm(skeleton = skeleton, target = target, model = 'empiric', 
                         beta_sd = beta_sd)
```

Fitting each to the same set of outcomes yields:

```{r}
fit3 <- model3 %>% fit(outcomes)
fit4 <- model4 %>% fit(outcomes) 
```

```{r}
fit3 %>% summary()
```

```{r}
fit4 %>% summary()
```

In this example, the model estimates are broadly consistent across methodology and model type.
However, this is not the general case.
To illustrate this point, let us examine a two parameter logistic model fit using `trialr` (note: this model is not implemented in `dfcrm`):


```{r}
model5 <- get_trialr_crm(skeleton = skeleton, target = target, model = 'logistic2', 
                         alpha_mean = 0, alpha_sd = 2, beta_mean = 0, beta_sd = 1)
fit5 <- model5 %>% fit(outcomes)
fit5 %>% summary()
```

Now the estimate of toxicity at the highest dose is high relative to the other models.
The extra free parameter in the two-parameter model offers more flexibility.
There has been a debate in the literature about one-parameter vs two-parameter models (and possibly more).
It is generally accepted that a single parameter model is too simplistic to accurately estimate $p_i$ over the entire dose range.
However, it will be sufficient to identify the dose closest to $p_T$, and if that is the primary objective of the trial, the simplicity of a one-parameter model may be entirely justified.
The interested reader is directed to @OQuigley1990 and @Neuenschwander2008.

Note that the CRM does not natively implement stopping rules, so these classes on their own will always advocate trial continuance:

```{r}
fit1 %>% continue()
fit5 %>% continue()
```

and identify each dose as admissible:

```{r}
fit1 %>% dose_admissible()
```

This behaviour can be altered by appending classes to advocate stopping for consensus:

```{r}
model6 <- get_trialr_crm(skeleton = skeleton, target = 0.3, model = 'empiric', 
                         beta_sd = 1) %>% 
  stop_when_n_at_dose(dose = 'recommended', n = 6)

fit6 <- model6 %>% fit('2NNN 3TTT 2NTN')

fit6 %>% continue()
fit6 %>% recommended_dose()
```

Or for stopping under excess toxicity:

```{r}
model7 <- get_trialr_crm(skeleton = skeleton, target = 0.3, model = 'empiric', 
                         beta_sd = 1) %>% 
  stop_when_too_toxic(dose = 1, tox_threshold = 0.3, confidence = 0.8)

fit7 <- model7 %>% fit('1NTT 1TTN')

fit7 %>% continue()
fit7 %>% recommended_dose()
fit7 %>% dose_admissible()
```

Or both:

```{r}
model8 <- get_trialr_crm(skeleton = skeleton, target = 0.3, model = 'empiric', 
                         beta_sd = 1) %>% 
  stop_when_n_at_dose(dose = 'recommended', n = 6) %>% 
  stop_when_too_toxic(dose = 1, tox_threshold = 0.3, confidence = 0.8)

```

For more information, check the package README and the other vignettes.

### dfcrm vs trialr
So which method should you use?
The answer depends on how you plan to use the models.

The `trialr` classes produce posterior samples:

```{r}
fit7 %>% 
  prob_tox_samples(tall = TRUE) %>% 
  head()
```

and these facilitate flexible visualisation:

```{r, message=FALSE}
library(ggplot2)
library(dplyr)

fit7 %>% 
  prob_tox_samples(tall = TRUE) %>% 
  mutate(.draw = .draw %>% as.integer()) %>% 
  filter(.draw <= 200) %>% 
  ggplot(aes(dose, prob_tox)) + 
  geom_line(aes(group = .draw), alpha = 0.2)
```

However, MCMC sampling is an expensive computational procedure compared to the numerical integration used in `dfcrm`.
If you envisage fitting lots of models, perhaps in simulations or dose-paths (see below) and favour a model offered by `dfcrm`, we recommend using `get_dfcrm`.
However, if you favour a model only offered by `trialr`, or if you are willing for calculation to be slow in order to get posterior samples, then use `trialr`.


## Dose paths

We can use the `get_dose_paths` function in `escalation` to calculate exhaustive model recommendations in response to every possible set of outcomes in future cohorts.
For instance, at the start of a trial using an empiric CRM, we can examine all possible paths a trial might take in the first two cohorts of three patients, starting at dose 2:

```{r, message=FALSE}
skeleton <- c(0.05, 0.12, 0.25, 0.40, 0.55)
target <- 0.25
beta_sd <- 1

model <- get_dfcrm(skeleton = skeleton, target = target, model = 'empiric', 
                   scale = beta_sd)
paths <- model %>% get_dose_paths(cohort_sizes = c(3, 3), next_dose = 2)
graph_paths(paths)
```

We see that the design would willingly skip dose 3 if no tox is seen in the first cohort.
This might warrant suppressing dose-dkipping by appending a `dont_skip_doses(when_escalating = TRUE)` selector.

Dose-paths can also be run for in-progress trials where some outcomes have been established.
For more information on working with dose-paths, refer to the dose-paths vignette.


## Simulation

We can use the `simulate_trials` function to calculate operating characteristics for a design.
Let us use the example above and tell the design to stop when the lowest dose is too toxic, when 9 patients have already been evaluated at the candidate dose, or when a sample size of $n=24$ is reached:

```{r}
model <- get_dfcrm(skeleton = skeleton, target = target, model = 'empiric', 
                   scale = beta_sd) %>% 
  stop_when_too_toxic(dose = 1, tox_threshold = target, confidence = 0.8) %>% 
  stop_when_n_at_dose(dose = 'recommended', n = 9) %>% 
  stop_at_n(n = 24)
```

For the sake of speed, we will run just fifty iterations:

```{r}
num_sims <- 50
```

In real life, however, we would naturally run many thousands of iterations.
Let us investigate under the following true probabilities of toxicity:

```{r}
sc1 <- c(0.25, 0.5, 0.6, 0.7, 0.8)
```

The simulated behaviour is:
```{r}
set.seed(123)
sims <- model %>%
  simulate_trials(num_sims = num_sims, true_prob_tox = sc1, next_dose = 1)

sims
```

We see that the chances of stopping for excess toxicity and recommending no dose is about 1-in-4.
Dose 1 is the clear favourite to be identified.
Interestingly, the `stop_when_n_at_dose` class reduces the expected sample size to 12-13 patints.
Without it:

```{r}
get_dfcrm(skeleton = skeleton, target = target, model = 'empiric', 
          scale = beta_sd) %>% 
  stop_when_too_toxic(dose = 1, tox_threshold = target, confidence = 0.8) %>% 
  stop_at_n(n = 24) %>% 
  simulate_trials(num_sims = num_sims, true_prob_tox = sc1, next_dose = 1)

```

expected sample size is much higher and the chances of erroneously stopping early are also higher.
These phenomena would justify a wider simulation study in a real situation.

For more information on running dose-finding simulations, refer to the simulation vignette.


# References