# Define Longitudinal Endpoints

The `TrialSimulator` package provides a flexible framework for defining endpoints in clinical trial simulations. Users can choose to generate endpoints using either built-in random number generators from standard probability distributions or custom user-defined functions. The ability to specify custom generators is particularly useful when simulating correlated endpoints, which often arise in longitudinal settings.

Longitudinal endpoints refer to outcomes measured repeatedly over time for the same subject, such as blood pressure, biomarker levels, or symptom severity scores. These repeated measures typically exhibit within-subject correlation as well as correlation with baseline values. Accurately simulating such correlation structures is essential for realistic trial design and evaluation.

In this vignette, we demonstrate how to define longitudinal endpoints using the `endpoint()` function, focusing on its `generator` argument to specify the data-generating mechanism, and the `readout` argument to determine when each endpoint is observed.

## Example: Simulating Longitudinal Blood Pressure Endpoints

Suppose blood pressure is measured for each patient at baseline (week 0), and again at weeks 2 and 4 following randomization. We are interested in changes from baseline at weeks 2 and 4. A typical dataset at a milestone (e.g., after enrollment or follow-up) that can be accessed by calling `trial$get_locked_data(milestone_name)` may resemble the following:

    #> # A tibble: 6 × 3
    #>   baseline bp_cfb2 bp_cfb4
    #>      <dbl>   <dbl>   <dbl>
    #> 1     144.  -24.7    -21.0
    #> 2     148.  -22.2    -42.5
    #> 3     145.  -13.4    -30.4
    #> 4     148.   -8.03   -31.2
    #> 5     139.  -28.1    -24.4
    #> 6     133.  -18.8    -18.3

In this example, the variables `baseline`, `bp_cfb2`, and `bp_cfb4` are observed at weeks 0, 2, and 4, respectively. Therefore, we will define `readout = c(baseline = 0, bp_cfb2 = 2, bp_cfb4 = 4)` in the endpoint definition.

To simulate correlated values across time points, we assume that `baseline`, `bp2`, and `bp4` follow a multivariate normal distribution. The user must provide the mean vector and covariance matrix for these variables. Importantly, any custom generator function must accept `n` (number of observations) as its first argument, as required by TrialSimulator, otherwise it throws an error.

    library(mvtnorm)
    bp_generator <- function(n, bp_means, bp_vcov){
      dat <- rmvnorm(n, mean = bp_means, sigma = bp_vcov) %>%
        as.data.frame()
      names(dat) <- c('baseline', 'bp2', 'bp4')
      dat %>%
        mutate(
          bp_cfb2 = bp2 - baseline,
          bp_cfb4 = bp4 - baseline
        ) %>%
        select(baseline, bp_cfb2, bp_cfb4)
    }

This function assumes a trivariate normal distribution for blood pressure at baseline, week 2, and week 4. Users may also extend this function to support more complex simulation logic as needed.

## Defining Endpoints for an Arm

We now use the `endpoint()` function to define the longitudinal endpoints in a treatment arm. The required parameters for the custom generator, `bp_means` and `bp_vcov`, are passed through the ellipsis (`...`) argument.

    vcov1 <- matrix(
      c(2, 1.5, 1,
        1.5, 3, 1.5,
        1, 1.5, 4),
      nrow = 3
    )

    ep_in_trt1 <- endpoint(
      name = c('bp_cfb2', 'baseline', 'bp_cfb4'),
      type = rep('non-tte', 3),
      readout = c(baseline = 0, bp_cfb4 = 4, bp_cfb2 = 2),
      generator = bp_generator,
      bp_means = c(140, 125, 120),
      bp_vcov = vcov1
    )

Note that we deliberately specify the `name` and `readout` arguments in an order different from the data frame returned by `bp_generator`, to illustrate that the `endpoint()` function correctly maps variable names to their respective time points. However, the user is responsible for ensuring the order of `name` and `type` are aligned; the function cannot automatically infer types from the generator output.

This example also demonstrates that `endpoint()` can be used to generate covariates. For instance, the baseline value is typically used as a covariate in statistical models, and should therefore be simulated and retained. More generally, users can define covariates, biomarker dynamics, or subgroup indicators using the same mechanism to simulate complex trial designs.

A simple way to generate a report for an endpoint object is to print it in console directly

    ep_in_trt1

## Defining Endpoints for Another Arm

We now define another arm with slightly different means and correlation structure for blood pressure:

    vcov2 <- matrix(
      c(2, 1.5, 1,
        1.5, 3, 1,
        1, 1, 4),
      nrow = 3
    )

    ep_in_trt2 <- endpoint(
      name = c('bp_cfb2', 'baseline', 'bp_cfb4'),
      type = rep('non-tte', 3),
      readout = c(baseline = 0, bp_cfb4 = 4, bp_cfb2 = 2),
      generator = bp_generator,
      bp_means = c(140, 127, 122),
      bp_vcov = vcov2
    )

    ep_in_trt2

Note that user can even specify different generators across arms. For example, `bp_generator` can be replaced by another function with different mechanism and arguments.

## Creating Treatment Arms and Adding Endpoints

Finally, we create two treatment arms and attach the respective endpoints:

    trt1 <- arm(name = 'treatment 1')
    trt2 <- arm(name = 'treatment 2')

    trt1$add_endpoints(ep_in_trt1)
    trt2$add_endpoints(ep_in_trt2)

    trt1

    trt2