---
title: "Designs with Response-Adaptive Randomization"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Designs with Response-Adaptive Randomization}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  cache.path = 'cache/responseAdaptive/',
  comment = '#>',
  dpi = 300,
  out.width = '100%'
)
```

```{r setup, echo = FALSE, message = FALSE}
library(dplyr)
library(kableExtra)
library(DoseFinding)
library(TrialSimulator)
set.seed(12345)
```

Response-adaptive randomization (RAR) can be a powerful strategy in Phase II dose-finding trials. It allows sponsors to dynamically update the randomization scheme at one or more interim analyses based on accumulating data. By shifting allocation toward more promising treatment arms, RAR can enhance the ethical and statistical efficiency of the trial.

This vignette demonstrates how to simulate a trial with response-adaptive design using the `TrialSimulator` package. For further background, refer to [this](https://www.mediana.us/MedianaDesigner/ADRand.pdf) document from the `MedianaDesigner` package. Dr. Alex Dmitrienko also provides a series of excellent online lectures on this topic:

- [Part 1](https://www.youtube.com/watch?v=-ADsHASLimM&list=PL_qOo99xv_nRJU8MEBO_VzBjH8duFy01n&index=3&ab_channel=MedianaOnline)

- [Part 2](https://www.youtube.com/watch?v=2agD_9qP1cU&t=1447s&ab_channel=MedianaOnline)

However, the original `MedianaDesigner::ADRand()` function is no longer functional, even for examples provided on [this](https://medianasoft.github.io/CaseStudyF1) page. Therefore, this vignette focuses on implementing a similar response-adaptive design using `TrialSimulator`. The core algorithm for updating the randomization ratio is re-implemented based on the logic of the `DoseFinding` package and may differ slightly from that used in Dr. Dmitrienko's materials.

## Simulation Settings

- We assume an `Emax` model for the endpoint `fev1` (forced expiratory volume in 1 second) measured after 4 months of treatment. The maximum effect (0.1) is achieved at dose 100.

- The trial includes one placebo arm and five active arms with doses: 20, 25, 30, and 35.

  - Patients are initially randomized equally across all five arms.
  
- A total of 200 patients are recruited over 36 months, with 50% of enrollment expected by 24 months.

- Two interim analyses are planned after 50 and 120 patients have non-missing `fev1` readouts, i.e. pipeline patients are excluded. 
  
- The final analysis is performed when data from all 200 patients are available.

- At each interim: 

  - Candidate dose-response models `Emax`, `sigEmax`, and `quadratic` are fitted.
  
  - Bootstrap estimates from `DoseFinding::maFitMod()` are used to calculate, for each dose $d \in \{20, 25, 30, 35\}$, the probability $p_d$ that the estimated treatment effect exceeds 0.08.
  
  - The randomization ratio for each active dose is set proportional to $p_d$
  
  - The placebo ratio remains fixed at 20%.

- At the final analysis, a multiple contrast test is conducted using data from all 200 patients.

## Define Data Generator of `fev1`

The following function generates `fev1` outcomes using the assumed `Emax` model. It is later assigned as the generator function when defining endpoints.

```{r eaioajf}
rng <- function(n, dose){

  model <- DoseFinding::Mods(
    emax = c(2.6, 12.5),
    placEff = 1.25, maxEff = 0.1,
    doses = c(0, 20, 25, 50, 100))

  data.frame(
    fev1 = rnorm(n, mean = getResp(model, doses = dose), sd = .05)
  )

}
```

## Define `fev1` Endpoints for Each Arm

Each treatment arm is associated with an endpoint definition, specifying the dose and data generator.

```{r lalglafj}
fev1 <- endpoint(name = 'fev1', type = 'non-tte', readout = c(fev1 = 4),
                 generator = rng, dose = 0)
pbo <- arm(name = '0.0')
pbo$add_endpoints(fev1)

fev1 <- endpoint(name = 'fev1', type = 'non-tte', readout = c(fev1 = 4),
                 generator = rng, dose = 20.0)
dose1 <- arm(name = '20.0')
dose1$add_endpoints(fev1)

fev1 <- endpoint(name = 'fev1', type = 'non-tte', readout = c(fev1 = 4),
                 generator = rng, dose = 25.0)
dose2 <- arm(name = '25.0')
dose2$add_endpoints(fev1)

fev1 <- endpoint(name = 'fev1', type = 'non-tte', readout = c(fev1 = 4),
                 generator = rng, dose = 30.0)
dose3 <- arm(name = '30.0')
dose3$add_endpoints(fev1)

fev1 <- endpoint(name = 'fev1', type = 'non-tte', readout = c(fev1 = 4),
                 generator = rng, dose = 35.0)
dose4 <- arm(name = '35.0')
dose4$add_endpoints(fev1)
```


## Define a Trial

Here we define the trial object with 200 patients and an accrual period of 36 months. The total trial duration is extended to 40 months to account for a 4-month follow-up after last enrollment.

```{r weoieaf}
accrual_rate <- data.frame(end_time = c(24, Inf),
                           piecewise_rate = c(100/24, 100/12))
trial <- trial(
  name = 'Trial-3415', n_patients = 200,
  seed = 1727811904, duration = 40,
  enroller = StaggeredRecruiter, accrual_rate = accrual_rate,
  silent = TRUE
)

trial$add_arms(sample_ratio = rep(1, 5), pbo, dose1, dose2, dose3, dose4)
trial
```

<!-- Hide this helper function and display it in Appendix for better typesetting. -->
```{r helpers, echo=FALSE}
compute_sample_ratio <- function(data){

  data$dose <- as.numeric(data$arm)
  fit <- lm(fev1 ~ factor(dose) - 1, data = data)
  dose <- unique(sort(data$dose))
  mu_hat <- coef(fit)
  S_hat <- vcov(fit)

  suppressMessages(
    ma_fit <- DoseFinding::maFitMod(dose, mu_hat, S = S_hat,
                                    models = c("emax", "sigEmax", "quadratic"))
  )

  pred <- predict(ma_fit, doseSeq = c(0, 20, 25, 30, 35), summaryFct = NULL)
  prob <- apply(pred[, -1] - pred[, 1], 2, function(x){mean(x > .08)})
  sample_ratio <- c(.2, (1 - .2) * prob / sum(prob)) %>% unname()

  sample_ratio
}

multiple_contrast_test <- function(data){
  
  candidate_models <- DoseFinding::Mods(
    emax = c(2.6, 12.5), sigEmax = c(30.5, 3.5), quadratic = -0.00776,
    placEff = 1.25, maxEff = 0.15, doses = c(0, 20, 25, 30, 35))

  data$dose <- as.numeric(data$arm)
  test <- DoseFinding::MCTtest(dose = dose, resp = fev1,
                               models = candidate_models, data = data)
  
  ## at least one dose shows significant non-flatten pattern
  any(attr(test$tStat, 'pVal') < .05)

}
```

<!-- Hide this action function and display it later for better typesetting. -->

```{r stage_action, echo=FALSE}
stage_action <- function(trial, milestone_name){

  locked_data <- trial$get_locked_data(milestone_name)
  new_sample_ratio <- compute_sample_ratio(locked_data)

  trial$update_sample_ratio(arm_names = c('0.0', '20.0', '25.0', '30.0', '35.0'),
                            sample_ratios = new_sample_ratio)
  
  message(milestone_name, ': ')
  data.frame(table(locked_data$arm), new_sample_ratio) %>%
    setNames(c('dose', 'total_n', 'new_ratio')) %>% print()

}
```

<!-- Hide this action function and display it later for better typesetting. -->

```{r final_action, echo=FALSE}
final_action <- function(trial){

  locked_data <- trial$get_locked_data('final')
  
  message('final: ')
  data.frame(table(locked_data$arm)) %>%
    setNames(c('dose', 'total_n')) %>% print()

  trial$save(value = multiple_contrast_test(locked_data),
             name = 'MC_test')
}
```


## Define Milestones and Associated Actions

Three milestones are defined: two interim analyses and one final analysis. The same action is used for both interims, while a separate one is used for the final.

```{r gfdassfl}
stage1 <- milestone(name = 'stage 1',
                    when = eventNumber('fev1', n = 50),
                    action = stage_action, milestone_name = 'stage 1')

stage2 <- milestone(name = 'stage 2',
                    when = eventNumber('fev1', n = 120),
                    action = stage_action, milestone_name = 'stage 2')

final <- milestone(name = 'final',
                   when = eventNumber('fev1', n = 200),
                   action = final_action)
```

The `stage_action()` function is called at each interim milestone to lock current data and update sample ratios based on model-based probabilities. It utilities a helper function `compute_sample_ratio()` which can be found in the Appendix below. 


```{r ref.label='stage_action', eval=FALSE}
```


At the final milestone, the function `final_action()` performs the multiple contrast test and stores the result. It calls a helper function `multiple_contrast_test()`, which can be found in the Appendix below. 

```{r ref.label='final_action', eval=FALSE}
```

## Execute a Trial

After registering all milestones with a listener object, we simulate the trial using `controller$run()`.

```{r sdlatiow}
listener <- listener()
listener$add_milestones(stage1, stage2, final)

controller <- controller(trial, listener)
controller$run(n = 1, plot_event = TRUE, silent = TRUE)

output <- controller$get_output()

output %>% 
  kable(escape = FALSE) %>% 
  kable_styling(bootstrap_options = "striped", 
                full_width = FALSE,
                position = "left") %>%
  scroll_box(width = "100%")
```

In the output, the columns `n_event_<milestone>_<arms>` contain detailed information on observed events or sample sizes per arm at each milestone. It is evident that we have pipeline patients at both interims. 

```{r qeaeal}
output[, 'n_events_<stage 1>_<arms>']

output[, 'n_events_<stage 2>_<arms>']

output[, 'n_events_<final>_<arms>']
```


## Appendix: Codes of Helper Functions

For completeness, the full code of the helper functions `compute_sample_ratio()` and `multiple_contrast_test()` is included below, which determine the new sample ratio and performs the multiple contrast test. 

```{r ref.label='helpers', eval=FALSE}
```

