Advanced example

This vignette provides an advanced example on how to:

  1. Specify a trial design with setup_trial() using user-written functions for generating outcomes and drawing samples from the posterior distributions
  2. Transform the raw outcome estimates in a way appropriate for your needs
  3. Derive and specify prior probability distributions

The manual for setup_trial() contains another example (more succinctly annotated); see the Examples sections in ?setup_trial(). As an alternative to using custom functions, the convenience functions setup_trial_binom() and setup_trial_norm() may be used for simulating trials with binary, binomially distributed or continuous, normally distributed outcomes, using sensible defaults and flat priors.

This vignette is intended for advanced users and assume general knowledge of writing R functions and specifying prior distributions for (simple, conjugate) Bayesian models. Before reading this vignette, we highly encourage you to read the Basic examples vignette (see vignette("Basic-examples", "adaptr")) with more basic examples using the simplified setup_trial_binom() function and a thorough discussion of the settings applicable to all trial designs.


In this example, we set up a trial with three arms, one of which is a common control, and an undesirable binary outcome (e.g., mortality).

This examples creates a custom version of the setup_trial_binom() function using non-flat priors for the event rates in each arm (setup_trial_binom() uses flat priors), and returning event probabilities as percentages (instead of fractions), to illustrate the use of a custom function to summarise the raw outcome data.

While setup_trial() attempts to validate the custom functions by assessing their output during trial specification, some edge cases might elude this validation. We, therefore, urge users specifying custom functions to carefully test more complex functions themselves before actual use.

If you go through the trouble of writing a nice set of functions for generating outcomes and sampling from posterior distributions, please consider adding these to the package. This way, others can benefit from your work and it helps validate them. See how on the GitHub page under Contributing.

Although the user-written custom functions below do not depend on the adaptr package, as the first thing we load the package:

#> Loading 'adaptr' package v1.4.0.
#> For instructions, type 'help("adaptr")'
#> or see

–and then set the global seed to ensure reproducible results in the vignette:


We define the functions and (for illustration purposes and as a sanity check) print their outputs. They are, then, invoked by setup_trial() (in the final code chunk of this vignette).

Functions for generating outcomes

This function should take a single argument (allocs), a character vector containing the allocations (names of trial arms) of all patients included since the last adaptive analysis. The function must return a numeric vector, regardless of actual outcome type (so, e.g., categorical outcomes must be encoded as numeric). The returned numeric vector must be of the same length, and the values in the same order as allocs. That is, the third element of allocs specifies the allocation of the third patient randomised since the last adaptive analysis, and (correspondingly) the third element of the returned vector is that patient’s outcome.

It sounds complicated, but it becomes clearer when we actually specify the function (essentially a re-implementation of the built-in function used by setup_trial_binom()):

get_ys_binom_custom <- function(allocs) {
  # Binary outcome coded as 0/1 - prepare returned vector of appropriate length
  y <- integer(length(allocs))
  # Specify trial arms and true event probabilities for each arm
  # These values should exactly match those supplied to setup_trial
  # NB! This is not validated, so this is the user's responsibility
  arms <- c("Control", "Experimental arm A", "Experimental arm B")
  true_ys <- c(0.25, 0.27, 0.20)
  # Loop through arms and generate outcomes
  for (i in seq_along(arms)) {
    # Indices of patients allocated to the current arm
    ii <- which(allocs == arms[i])
    # Generate outcomes for all patients allocated to current arm
    y[ii] <- rbinom(length(ii), 1, true_ys[i])
  # Return outcome vector

To illustrate how the function works, we first generate random allocations for 50 patients using equal allocation probabilities, the default behaviour of sample(). By enclosing the call in parentheses, the resulting allocations are printed:

(allocs <- sample(c("Control", "Experimental arm A", "Experimental arm  B"),
                  size = 50, replace = TRUE))
#>  [1] "Control"             "Experimental arm  B" "Experimental arm  B"
#>  [4] "Experimental arm  B" "Experimental arm  B" "Experimental arm A" 
#>  [7] "Experimental arm A"  "Experimental arm A"  "Experimental arm A" 
#> [10] "Experimental arm A"  "Experimental arm  B" "Experimental arm  B"
#> [13] "Experimental arm  B" "Control"             "Experimental arm  B"
#> [16] "Control"             "Experimental arm A"  "Experimental arm A" 
#> [19] "Experimental arm A"  "Experimental arm  B" "Control"            
#> [22] "Control"             "Experimental arm  B" "Control"            
#> [25] "Experimental arm A"  "Control"             "Experimental arm A" 
#> [28] "Control"             "Experimental arm  B" "Experimental arm  B"
#> [31] "Control"             "Experimental arm  B" "Control"            
#> [34] "Control"             "Experimental arm A"  "Experimental arm  B"
#> [37] "Control"             "Experimental arm A"  "Experimental arm A" 
#> [40] "Experimental arm A"  "Experimental arm  B" "Experimental arm A" 
#> [43] "Control"             "Experimental arm  B" "Control"            
#> [46] "Control"             "Control"             "Control"            
#> [49] "Experimental arm A"  "Experimental arm A"

Next, we generate random outcomes for these patients:

(ys <- get_ys_binom_custom(allocs))
#>  [1] 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0
#> [39] 0 0 0 1 1 0 0 1 0 1 1 0

Functions for drawing posterior samples

The setup_trial_binom() function uses beta-binomial conjugate prior models in each arm, with beta(1, 1) priors. These priors are uniform (≈ “non-informative”) on the probability scale and corresponds to the same amount of information as provided by 2 patients (1 with and 1 without an event), described in greater detail by, e.g., Ryan et al, 2019 (10.1136/bmjopen-2018-024256).

Our custom function for generating posterior draws also uses beta-binomial conjugate prior models, but with informative priors. Informative priors may prevent undue influence of random, early fluctuations in a trial by pulling posterior estimates closer to the prior when limited data are available.

We seek relatively weakly informative priors centred on previous knowledge (or beliefs), and so before we can actually define a function for generating posterior draws based on informative priors, we need to derive such a prior.

Informative priors

We assume that we have prior knowledge corresponding to a belief that the best estimate for the true event probability in the control arm is 0.25 (25%), and that the true event probability is between 0.15 and 0.35 (15-35%) with 95% probability. The mean of a beta distribution is simply [number of events]/[number of patients].

To derive a beta distribution that reflects this prior belief, we use find_beta_params(), a helper function included in the adaptr (see ?find_beta_params for details):

  theta = 0.25, # Event probability
  boundary = "lower",
  boundary_target = 0.15,
  interval_width = 0.95
#>   alpha beta      p2.5     p50.0     p97.5
#> 1    15   45 0.1498208 0.2472077 0.3659499

We thus see that our prior belief prior roughly corresponds to previous randomisation of 60 patients of whom 15 (alpha) experienced the event and 45 (beta) did not.

Even though we may expect event probabilities to differ in the non-control arms, for this example we consider this prior appropriate for all arms as we consider event probabilities smaller/larger than those represented by the prior unlikely.

Below, we illustrate the effects of this prior compared to the default beta(1, 1) prior used in setup_trial_binom() for a single trial arm with 20 patients randomised, 12 events and 8 non-events. This corresponds to an estimated event probability of 0.6 (60%), far more than the expected 0.25 (25%). This could come about by random fluctuations when few patients have been randomised, even if our prior beliefs are correct.

Next, we illustrate the effects on the same prior after 200 patients have been randomised to the same arm, with 56 events and 144 non-events, which corresponds to an estimated event probability of 0.28 (28%), which is more similar to the expected event probability.

When comparing to the previous plot, we clearly see that once more patients have been randomised, the larger sample of observed data starts to dominate the posterior, so that the prior exerts less influence on the posterior distribution (both posterior distributions are very alike despite the very different prior distributions).

Defining the function to generate posterior draws

There are a number of important things to be aware of when specifying this function. First, it must accept all the following arguments (with the exact same names, even if they are not used in the function):

Alternatively, unused arguments can be left out and the ellipsis (...) included as the final argument in your function.

Second, the order of allocs and ys must match: the fifth element of allocs represents the allocation of the fifth patient, while the fifth element of ys represent the outcome of the same patient.

Third, allocs and ys are provided for all patients, including those randomised to arms that are no longer active. This is done as users in some situations may want to use these data when generating posterior draws for the other (and currently active) arms.

Fourth, adaptr does not restrict how posterior samples are drawn. Consequently, Markov chain Monte Carlo- or variational inference-based methods may be used, and other packages supplying such functionality may be called by user-provided functions. However, using more complex methods than simple conjugate models substantially increases simulation run time. Consequently, simpler models are well-suited for use in simulations.

Fifth, the function must return a matrix of numeric values with length(arms) columns and n_draws rows, with the currently active arms as column names. That is, each row must contain one posterior draw for each arm. NA’s are not allowed, so even if no patients have been randomised to an arm yet, valid numeric values should be returned (e.g., drawn from the prior or from another very diffuse posterior distribution). Even if the outcome is not truly numeric, both the vector of outcomes provided to the function (ys) and the returned matrix with posterior draws must be encoded as numeric.

With this in mind, we are ready to specify the function:

get_draws_binom_custom <- function(arms, allocs, ys, control, n_draws) {
  # Setup a list to store the posterior draws for each arm
  draws <- list()
  # Loop through the ACTIVE arms and generate posterior draws
  for (a in arms) {
    # Indices of patients allocated to the current arm
    ii <- which(allocs == a)
    # Sum the number of events in the current arm
    n_events <- sum(ys[ii])
    # Compute the number of patients in the current arm
    n_patients <- length(ii)
    # Generate draws using the number of events, the number of patients
    # and the prior specified above: beta(15, 45)
    # Saved using the current arms' name in the list, ensuring that the
    # resulting matrix has column names corresponding to the ACTIVE arms
    draws[[a]] <- rbeta(n_draws, 15 + n_events, 45 + n_patients - n_events)
  # Bind all elements of the list column-wise to form a matrix with
  # 1 named column per ACTIVE arm and 1 row per posterior draw.
  # Multiply result with 100, as we're using percentages and not proportions
  # in this example (just to correspond to the illustrated custom function to
  # generate RAW outcome estimates below), draws) * 100

We now call the function using the previously generated allocs and ys.

To avoid cluttering, we only generate 10 posterior draws from each arm in this example:

  # Only currently ACTIVE arms, but all are considered active at this time
  arms = c("Control", "Experimental arm A", "Experimental arm B"),
  allocs = allocs, # Generated above
  ys = ys, # Generated above
  # Input control arm, argument is supplied even if not used in the function
  control = "Control",
  # Input number of draws (for brevity, only 10 draws here)
  n_draws = 10
#>        Control Experimental arm A Experimental arm B
#>  [1,] 30.96555           29.34973           29.26143
#>  [2,] 30.47382           23.22668           25.08249
#>  [3,] 31.04807           31.76577           19.81416
#>  [4,] 17.00712           24.30809           16.36256
#>  [5,] 21.31251           27.74615           22.63147
#>  [6,] 25.50944           24.16283           30.29049
#>  [7,] 16.60420           29.49526           28.75436
#>  [8,] 25.17899           33.29374           30.87149
#>  [9,] 23.72043           27.78537           29.89836
#> [10,] 30.50004           28.43694           26.62115

Importantly, less than 100 posterior draws from each arm is not allowed when setting up the trial specification, to avoid unstable results (see setup_trial_binom()).

Specifying the function to calculate raw outcome estimates

Finally, a custom function may be specified to calculate raw summary estimates in each arm; these raw estimates are not posterior estimates, but can be considered the maximum likelihood point estimates in this example. This function must take a numeric vector (all outcomes in an arm) and return a single numeric value. This function is called separately for each arm. Because we express results as percentages and not proportions in this example, this function simply calculates the outcome percentage in each arm:

fun_raw_est_custom <- function(ys) {
  mean(ys) * 100

We now call this function on the outcomes in the "Control" arm, as an example:

  "Raw outcome percentage estimate in the 'Control' group: %.1f%%", 
  fun_raw_est_custom(ys[allocs == "Control"])
#> Raw outcome percentage estimate in the 'Control' group: 29.4%

Setup the trial specification

With all the functions defined, we can now setup the trial specification. As stated above, some validation of all the custom functions is carried out when the trial is setup:

  arms = c("Control", "Experimental arm A", "Experimental arm B"),
  # true_ys, true outcome percentages (since posterior draws and raw estimates
  # are returned as percentages, this must be a percentage as well, even if
  # probabilities are specified as proportions internally in the outcome
  # generating function specified above
  true_ys = c(25, 27, 20),
  # Supply the functions to generate outcomes and posterior draws
  fun_y_gen = get_ys_binom_custom,
  fun_draws = get_draws_binom_custom,
  # Define looks
  max_n = 2000,
  look_after_every = 100,
  # Define control and allocation strategy
  control = "Control",
  control_prob_fixed = "sqrt-based",
  # Define equivalence assessment - drop non-control arms at > 90% probability
  # of equivalence, defined as an absolute difference of 10 %-points
  # (specified on the percentage-point scale as the rest of probabilities in
  # the example)
  equivalence_prob = 0.9,
  equivalence_diff = 10,
  equivalence_only_first = TRUE,
  # Input the function used to calculate raw outcome estimates
  fun_raw_est = fun_raw_est_custom,
  # Description and additional information
  description = "custom trial [binary outcome, weak priors]",
  add_info = "Trial using beta-binomial conjugate prior models and beta(15, 45) priors in each arm."
#> Trial specification: custom trial [binary outcome, weak priors]
#> * Undesirable outcome
#> * Common control arm: Control 
#> * Control arm probability fixed at 0.414 (for 3 arms), 0.5 (for 2 arms)
#> * Best arm: Experimental arm B
#> Arms, true outcomes, starting allocation probabilities 
#> and allocation probability limits:
#>                arms true_ys start_probs fixed_probs min_probs max_probs
#>             Control      25       0.414       0.414        NA        NA
#>  Experimental arm A      27       0.293          NA        NA        NA
#>  Experimental arm B      20       0.293          NA        NA        NA
#> Maximum sample size: 2000 
#> Maximum number of data looks: 20
#> Planned looks after every 100
#>  patients have reached follow-up until final look after 2000 patients
#> Number of patients randomised at each look:  100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000
#> Superiority threshold: 0.99 (all analyses)
#> Inferiority threshold: 0.01 (all analyses)
#> Equivalence threshold: 0.9 (all analyses) (only checked for first control)
#> Absolute equivalence difference: 10
#> No futility threshold
#> Soften power for all analyses: 1 (no softening)
#> Additional info: Trial using beta-binomial conjugate prior models and beta(15, 45) priors in each arm.

As setup_trial() runs with no errors or warnings, the custom trial has been successfully specified and may be run by run_trial() or run_trials() or calibrated by calibrate_trial(). If the custom functions provided to setup_trial() calls other custom functions (or uses objects defined by the user outside these functions) or if functions loaded from non-base R packages are used, please be aware that exporting these objects/functions or prefixing them with their namespace is necessary if simulations are conducted using multiple cores. See setup_cluster() and run_trial() for additional details on how to export necessary functions and objects.