# Simulate Trials with Dynamic Treatment Switching

The `TrialSimulator` package can handle three types of crossover designs, of which dynamic treatment switching is common in clinical trial, but simulating it is usually an unmet need due to its complexity. This vignette demonstrates how to simulate a trial where all or a subset of patients are offered the option of treatment switching over time. This happens in settings such as:

- crossover after progression, for example control patients switching to active treatment;
- rescue medication after non-response;
- dose escalation or dose reduction based on response or toxicity;
- switching after a adverse event to a different regimen.

These examples differ clinically, but they share the same simulation challenge: the patient does not follow a single static treatment path from baseline to the end of follow-up. In many cases, switching is not just a change in treatment label. It changes the patient’s future outcome trajectory. For example, switching may alter post-switch survival, response probability, or adverse-event risk, while leaving earlier outcomes unchanged. To address this challenge in simulation, `TrialSimulator` offers to define a `regimen` object in which we can implement custom mechanism for crossover.

Note that there are many other types of crossover that cannot be simulated using the approach discussed in this vignette. For example, patients may be reassigned after the arm is dropped. In such a scenario, the availability of alternative regimen depend on analysis result of unblinded data and can only happen at interim. We will show how to do that in other vignette. For crossover design with wash-out periods, please refer to this vignette.

## A Common Structure

Most switching designs can be reduced to three questions:

- Who switches and what’s the new regimen?
- When does switching occur?
- What changes after switching?

This leads naturally to a two-stage simulation strategy:

1. generate patient data under the originally assigned arm for all enrolled patients;
2. apply a patient-level switching mechanism that updates post-switch outcomes.

This is the core idea behind `regimen` in `TrialSimulator`.

## Why `regimen` uses three functions?

The `regimen` interface splits treatment switching into three components:

- `what()`, a treatment allocator for switchers
- `when()`, a time selector for switching
- `how()`, a data modifier altering post-switch outcome trajectory

A single large switching function could do all of this, but it would mix different responsibilities and become difficult to reuse. In practice, these components often vary separately across scenarios. For example, one may keep the same switching population but compare different switching times, or keep selection and timing fixed while testing different post-switch assumptions. The three-function design keeps these choices explicit.

All the three functions take a data frame `patient_data` as input argument. It is the data generated automatically by calling `generator` of endpoints for the whole life-cycle of the trial. The package will figure out patients who are eligible for the three functions, and the right timing to call.

## The Role of `what()`

This custom function is responsible for identifying switchers and assigning their new treatment regimen. Some typical rules that we can consider include:

- only patients in a control arm can switch;
- only non-responders receive rescue therapy;
- switching destination depends on subgroup, biomarker, etc.

The output of `what()` consists of two columns `patient_id` and `new_treatment`, with one row per switcher. Patients not included simply remain under their original treatment path and outcome trajectory.

## The Role of `when()`

This custom function assigns a switching time to each selected patient. A patient may switch:

- at progression,
- if not respond,
- at a scheduled visit,
- once a composite condition is met, etc.

Switching time can depend on the full outcome trajectory. For example, patient may have a higher chance of switching (e.g., for rescue) when being closer to the onset of major event (e.g., death). Keeping timing in `when()` makes scenario analysis much easier. The same switching population can be reused under different timing assumptions.

Note that patients whose data is passed to `when()` is a subset of patients whose data is passed to `what()`. The package governs this to prevent logic error and avoid unnecessary computation.

This function returns a data frame of two columns `patient_id` and `switch_time` measured from enrollment. All selected patients must be assigned a switching time, otherwise package will throw an error.

## The Role of `how()`

This custom function modifies patient data to reflect the consequences of switching. In practice, switching does not necessarily mean replacing the patient with a whole new patient from another arm. More often, we wants to

- keep pre-switch history unchanged;
- modify only what happens after the switch;
- update only selected endpoints;
- allow different post-switch behavior for different new treatments, etc.

So `how()` is not just a label change. It is a controlled post-switch data update. Depending on the simulation goal, `how()` could:

- extend residual survival time after switching;
- change the post-switch hazard rate;
- replace a future event time with a draw from another model;
- modify response probability after switching;
- leave some endpoints unchanged if they are not used in formal testing or decision making, etc.

This function returns a data frame of `patient_id` and columns of endpoints that are altered. We do not have to return an endpoint if it is not updated for any patient. For an endpoint that is updated for only a subset of switchers, return the unchanged cells at their original value – the natural `ifelse(condition, new_value, original)` pattern. Note that we should never apply dropout or censoring manually in `how()` because `TrialSimulator` will handle that automatically when triggering milestones and prepare locked data in action functions.

The three-functions design in `regimen` provides a useful balance between structure and flexibility. The interface mirrors the way investigators naturally describe switching designs. It encourages modular thinking because each part can be changed independently. Sensitivity analysis becomes straightforward under this framework as we can modify one function at a time. Another benefit is that more than one set of treatment allocator, time selector and data modifier are accepted for simulating multi-stage crossover. It also avoids overloading the arm definitions and supports partial data updates.

This flowchart illustrates how `regimen` is integrated with other modules in `TrialSimulator`.

## Implement Dynamic Treatment Switching

Dynamic treatment switching can be added to any simulation codes implemented with `TrialSimulator`. So we only show code snippets in this vignette to illustrate what do the three functions look like in several examples.

In all cases, we need to implement functions to initialize an `regimen` object

    treatment_allocator <- function(patient_data){...}
    time_selector <- function(patient_data){...}
    data_modifier <- function(patient_data){...}

    regimen <- regimen(treatment_allocator, time_selector, data_modifier)

All three functions must have `patient_data` as argument otherwise `TrialSimulator` will throw exception. The regime is then registered to a trial before any arm is added to it.

    trial <- trial(...)
    trial$add_regime(regimen)

Any codes like below will trigger an error message because `trial$add_arms(...)` enrolls patients into a trial immediately but we need to make `regimen` ready before then.

    trial <- trial(...)
    trial$add_arms(sample_ratio, soc, low_dose, high_dose)
    trial$add_regime(regimen)

    #> Error in trial$add_regimen(regimen) :
    #>   Member function trial$add_regimen() must be called before trial$add_arms(). A good practice is to call trial$add_regimen() immediately after trial() is executed.

#### Example 1: Crossover after progression

In oncology trials, patients initially randomized to standard of care (`soc`) may be allowed to switch to active treatment after disease progression, conditional on a patient-level event. If we assume 30% of the patients are assigned to low dose while 40% of the patients are assigned to high dose, we can implement the `what()` function like

    treatment_allocator <- function(patient_data){
      ## add break point to develop and debug
      # browser()
      switch_to <- sample(c('low', 'high', 'stay'), nrow(patient_data),
                            replace = TRUE, prob = c(.3, .4, .3))
      ## placebo patients who progressed before death (pfs < os) and were
      ## assigned a dose are the switchers; the rest are simply not returned
      is_switcher <- patient_data$arm == 'placebo' &
        patient_data$pfs < patient_data$os & switch_to != 'stay'
      sw <- patient_data[is_switcher, ]
      data.frame(
        patient_id    = sw$patient_id,
        new_treatment = ifelse(switch_to[is_switcher] == 'low', 'low dose', 'high dose')
      )

    }

The `when()` function simply returned the progression time, i.e., switching does not relies on event after the switching time.

    time_selector <- function(patient_data){
      ## add break point to develop and debug
      # browser()
      data.frame(
        patient_id = patient_data$patient_id,
        ## all patient in patient_data progress before die
        ## thus pfs < os and can switch.
        ## See treatment_allocator()
        switch_time = patient_data$pfs
      )

    }

Another common way to specify the switching time is picking a time point between `pfs` and `os`, i.e.,

    time_selector <- function(patient_data){
      ## add break point to develop and debug
      # browser()
      data.frame(
        patient_id = patient_data$patient_id,
        ## all patient in patient_data progress before die
        ## thus pfs < os and can switch.
        ## See treatment_allocator()
        switch_time = runif(nrow(patient_data), min = patient_data$pfs, max = patient_data$os)
      )

    }

In the `how()` function, we extend residual survival time by a factor using the causal accelerated failure time model depending on the assigned dose. Here we only update the overall survival `os` and leave other endpoints (e.g., response status) or variables unchanged, assuming that we won’t use their values after switching when summarizing the simulation.

    data_modifier <- function(patient_data){
      ## add break point to develop and debug
      # browser()
      f <- ifelse(patient_data$new_treatment == 'low dose', 1.1, 1.15)
      data.frame(
        patient_id = patient_data$patient_id,
        ## other_endpoint = ...,
        os = patient_data$switch_time + f * (patient_data$os - patient_data$switch_time)
      )

    }

#### Example 2: Crossover for non-responsers

Here we consider a trial of a placebo arm and two active treatment arms (low and high dose). Patients in the low dose arm who do not respond are switched to a higher dose.

    treatment_allocator <- function(patient_data){
      ## add break point to develop and debug
      # browser()
      ## low-dose non-responders switch to high dose
      sw <- patient_data[patient_data$arm == 'low dose' & patient_data$response == 0, ]
      data.frame(
        patient_id    = sw$patient_id,
        new_treatment = 'high dose'
      )

    }

When implementing time selector for the function `when()`, the switching time is set to the readout time

    time_selector <- function(patient_data){
      ## add break point to develop and debug
      # browser()
      data.frame(
        patient_id = patient_data$patient_id,
        ## all patient in patient_data progress before die
        ## thus pfs < os and can switch
        switch_time = patient_data$response_readout
      )

    }

### Example 3: Crossover when condition deteriorates

Now we assume that all patients in the placebo arm can crossover when their condition deteriorate significantly. Specifically, the switching time is 1 month before a patient dies. If the overall survival is shorter than a month, the switching time is set to `0.9 * os`.

    treatment_allocator <- function(patient_data){
      ## add break point to develop and debug
      # browser()
      ## all placebo patients switch
      sw <- patient_data[patient_data$arm == 'placebo', ]
      data.frame(
        patient_id    = sw$patient_id,
        new_treatment = 'new treatment'
      )

    }

    time_selector <- function(patient_data){
      ## add break point to develop and debug
      # browser()
      data.frame(
        patient_id = patient_data$patient_id,
        ## all patient in patient_data progress before die
        ## thus pfs < os and can switch
        switch_time = ifelse(patient_data$os <= 1, .9 * patient_data$os, patient_data$os - 1)
      )

    }

### Example 4: Crossover multiple times

We can implement multiple rounds of crossover for each patient. To do this, simply provide `regimen()` with a list of treatment allocators for `what`, a list of time selectors for `when`, and a list of data modifiers for `how`.

    what <- list(allocator1, allocator2, allocator3)
    when <- list(selector1, selector2, selector3)
    how <- list(modifier1, modifier2, modifier3)

    regimen <- regimen(what, when, how)

    trial <- trial(...)
    trial$add_regimen(regimen)