This vignette provides examples of various trial specifications using different combinations of settings, including various randomisation strategies including fixed randomisation, response-adaptive randomisation (RAR), and combinations.

The general-purpose function for specifying a trial is
`setup_trial()`

, but because trials with binary, binomially
distributed and continuous, normally distributed outcomes are so common,
the package comes with two convenience functions for specifying such
trial designs (using default priors only):
`setup_trial_binom()`

and
`setup_trial_norm()`

.

To keep things simple, this vignette uses only the
`setup_trial_binom()`

function and focuses on settings that
apply to trial designs regardless of outcome type. The code is heavily
annotated, but comments focus on settings not touched on earlier in the
vignette (e.g. we do not keep annotating the `arm`

and
`true_ys`

arguments).

Keep in mind that the `calibrate_trial()`

function can be
used to calibrate a trial specification to obtain a specific value for a
certain performance metric (e.g., the Bayesian type 1 error rate for
trial specifications with no between-arm differences).

For a general overview of how to use the `adaptr`

package,
please see `vignette("Overview", "adaptr")`

.

An **advanced** example on how to specify a trial design
with `setup_trial()`

, including the use of custom functions
for generating outcomes and yielding posterior draws, is provided in
`vignette("Advanced-example", "adaptr")`

.

First, the package is loaded:

`library(adaptr)`

In this section, several examples for trials *without* a
common control arm are provided. General settings applicable for all
trial designs (including both trial specifications with and without a
common control arm) are covered in this section.

```
setup_trial_binom(
# Four arms
arms = c("A", "B", "C", "D"),
# Set true outcomes (in this example event probabilities) for all arms
true_ys = c(0.3, 0.35, 0.31, 0.27), # 30%, 34%, 31% and 27%, respectively
# Set starting allocation probabilities
# Defaults to equal allocation if not specified
start_probs = c(0.3, 0.3, 0.2, 0.2),
# Set fixed allocation probability for first arm
# NA corresponds to no limits for specific arms
# Default (NULL) corresponds to no limits for all arms
fixed_probs = c(0.3, NA, NA, NA),
# Set minimum and maximum probability limits for some arms
# NA corresponds to no limits for specific arms
# Default (NULL) corresponds to no limits for all arms
# Must be NA for arms with fixed_probs (first arm in this example)
# sum(fixed_probs) + sum(min_probs) must not exceed 1
# sum(fixed_probs) + sum(max_probs) may be greater than 1, and must be at least
# 1 if specified for all arms
min_probs = c(NA, 0.2, NA, NA),
max_probs = c(NA, 0.7, NA, NA),
# Set looks - alternatively, specify both max_n AND look_after_every
data_looks = seq(from = 300, to = 1000, by = 100),
# No common control arm (as default, but explicitly specified in this example)
control = NULL,
# Set inferiority/superiority thresholds (different values than the defaults)
# (see also the calibrate_trial() function)
inferiority = 0.025,
superiority = 0.975,
# Define that the outcome is desirable (as opposed to the default setting)
highest_is_best = TRUE,
# No softening (the default setting, but made explicit here)
soften_power = 1,
# Use different simulation/summary settings than default
cri_width = 0.89, # 89% credible intervals
n_draws = 1000, # Only 1000 posterior draws in each arm
robust = TRUE, # Summarise posteriors using medians/MAD-SDs (as default)
# Trial description (used by print methods)
description = "example trial specification 1"
)#> Trial specification: example trial specification 1
#> * Desirable outcome
#> * No common control arm
#> * Best arm: B
#>
#> Arms, true outcomes, starting allocation probabilities
#> and allocation probability limits:
#> arms true_ys start_probs fixed_probs min_probs max_probs
#> A 0.30 0.3 0.3 NA NA
#> B 0.35 0.3 NA 0.2 0.7
#> C 0.31 0.2 NA NA NA
#> D 0.27 0.2 NA NA NA
#>
#> Maximum sample size: 1000
#> Maximum number of data looks: 8
#> Planned data looks after: 300, 400, 500, 600, 700, 800, 900, 1000 patients have reached follow-up
#> Number of patients randomised at each look: 300, 400, 500, 600, 700, 800, 900, 1000
#>
#> Superiority threshold: 0.975 (all analyses)
#> Inferiority threshold: 0.025 (all analyses)
#> No equivalence threshold
#> No futility threshold (not relevant - no common control)
#> Soften power for all analyses: 1 (no softening)
```

- No common control arm
- Equivalence testing
- Different softening powers (decreasing softening as the trial progresses)
- Default settings for many unspecified arguments

```
setup_trial_binom(
# Specify arms and true outcome probabilities (undesirable outcome as default)
arms = c("A", "B", "C", "D"),
true_ys = c(0.2, 0.22, 0.24, 0.18),
# Specify adaptive analysis looks using max_n and look_after_every
# max_n does not need to be a multiple of look_after_every - a final look
# will be conducted at max_n regardless
max_n = 1250, # Maximum 1250 patients
look_after_every = 100, # Look after every 100 patients
# Assess equivalence between all arms: stop if >90 % probability that the
# absolute difference between the best and worst arms is < 5 %-points
# Note: equivalence_only_first must be NULL (default) in designs without a
# common control arm (such as this trial)
equivalence_prob = 0.9,
equivalence_diff = 0.05,
# Different softening powers at each look (13 possible looks in total)
# Starts at 0 (softens all allocation probabilities to be equal) and ends at
# 1 (no softening) for the final possible look in the trial
soften_power = seq(from = 0, to = 1, length.out = 13)
)#> Trial specification: generic binomially distributed outcome trial
#> * Undesirable outcome
#> * No common control arm
#> * Best arm: D
#>
#> Arms, true outcomes, starting allocation probabilities
#> and allocation probability limits:
#> arms true_ys start_probs fixed_probs min_probs max_probs
#> A 0.20 0.25 NA NA NA
#> B 0.22 0.25 NA NA NA
#> C 0.24 0.25 NA NA NA
#> D 0.18 0.25 NA NA NA
#>
#> Maximum sample size: 1250
#> Maximum number of data looks: 13
#> Planned looks after every 100
#> patients have reached follow-up until final look after 1250 patients
#> Number of patients randomised at each look: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1250
#>
#> Superiority threshold: 0.99 (all analyses)
#> Inferiority threshold: 0.01 (all analyses)
#> Equivalence threshold: 0.9 (all analyses) (no common control)
#> Absolute equivalence difference: 0.05
#> No futility threshold (not relevant - no common control)
#> Soften power for each consequtive analysis: 0, 0.083, 0.167, 0.25, 0.333, 0.417, 0.5, 0.583, 0.667, 0.75, 0.833, 0.917, 1
```

In this section, several examples for trials *with* a common
control arm are provided and focus mostly on options specific to trial
designs with a common control arm.

- A common control arm
*square-root-transformation-based*fixed allocation probabilities (see description in`setup_trial()`

)- Assessment of both equivalence and futility compared to the initial control only (not assessed for superior arms that become subsequent controls)

```
setup_trial_binom(
arms = c("A", "B", "C", "D"),
# Specify control arm
control = "A",
true_ys = c(0.2, 0.22, 0.24, 0.18),
data_looks = seq(from = 100, to = 1000, by = 100),
# Fixed, square-root-transformation-based allocation throughout
control_prob_fixed = "sqrt-based fixed",
# Assess equivalence: drop non-control arms if > 90% probability that they
# are equivalent to the common control, defined as an absolute difference of
# < 3 %-points
equivalence_prob = 0.9,
equivalence_diff = 0.03,
# Only assess against the initial control (i.e., not assessed if an arm is
# declared superior to the initial control and becomes the new control)
equivalence_only_first = TRUE,
# Assess futility: drop non-control arms if > 80% probability that they are
# < 10 %-points better (in this case lower because outcome is undesirable in
# this example) compared to the common control
futility_prob = 0.8,
futility_diff = 0.1,
# Only assessed for the initial control, as described above
futility_only_first = TRUE
)#> Trial specification: generic binomially distributed outcome trial
#> * Undesirable outcome
#> * Common control arm: A
#> * Control arm probability fixed at 0.366 (for 4 arms), 0.414 (for 3 arms), 0.5 (for 2 arms)
#> * Best arm: D
#>
#> Arms, true outcomes, starting allocation probabilities
#> and allocation probability limits:
#> arms true_ys start_probs fixed_probs min_probs max_probs
#> A 0.20 0.366 0.366 NA NA
#> B 0.22 0.211 0.211 NA NA
#> C 0.24 0.211 0.211 NA NA
#> D 0.18 0.211 0.211 NA NA
#>
#> Maximum sample size: 1000
#> Maximum number of data looks: 10
#> Planned data looks after: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 patients have reached follow-up
#> Number of patients randomised at each look: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000
#>
#> 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: 0.03
#> Futility threshold: 0.8 (all analyses) (only checked for first control)
#> Absolute futility difference (in beneficial direction): 0.1
#> Soften power for all analyses: 1 (no softening - all arms fixed)
```

*Square-root-transformation-based*initial allocation probabilities*Square-root-transformation-based*allocation to the control arm (including subsequent controls, if a non-control arm is declared superior to the initial control)- Restricted response-adaptive randomisation to the non-control arms

```
setup_trial_binom(
arms = c("A", "B", "C", "D"),
control = "A",
true_ys = c(0.2, 0.22, 0.24, 0.18),
data_looks = seq(from = 100, to = 1000, by = 100),
# Square-root-transformation-based control arm allocation including for
# subsequent controls and initial equal allocation to the non-control arms,
# followed by response-adaptive randomisation
control_prob_fixed = "sqrt-based",
# Restricted response-adaptive randomisation
# Minimum probabilities of 20% for non-control arms, must be NA for the
# control arm with fixed allocation probability
# Limits are ignored for arms that become subsequent controls
# Limits are rescaled (i.e., increased proportionally) when arms are dropped
min_probs = c(NA, 0.2, 0.2, 0.2),
rescale_probs = "limits",
# Constant softening of 0.5 (= square-root transformation)
soften_power = 0.5
)#> Trial specification: generic binomially distributed outcome trial
#> * Undesirable outcome
#> * Common control arm: A
#> * Control arm probability fixed at 0.366 (for 4 arms), 0.414 (for 3 arms), 0.5 (for 2 arms)
#> * Best arm: D
#>
#> Arms, true outcomes, starting allocation probabilities
#> and allocation probability limits (min/max_probs rescaled):
#> arms true_ys start_probs fixed_probs min_probs max_probs
#> A 0.20 0.366 0.366 NA NA
#> B 0.22 0.211 NA 0.2 NA
#> C 0.24 0.211 NA 0.2 NA
#> D 0.18 0.211 NA 0.2 NA
#>
#> Maximum sample size: 1000
#> Maximum number of data looks: 10
#> Planned data looks after: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 patients have reached follow-up
#> Number of patients randomised at each look: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000
#>
#> Superiority threshold: 0.99 (all analyses)
#> Inferiority threshold: 0.01 (all analyses)
#> No equivalence threshold
#> No futility threshold
#> Soften power for all analyses: 0.5
```

This example is similar to that above (with different restriction
settings), but only use *square-root-transformation-based*
allocation probabilities to the *initial* control arm. Hence,
this will not apply if another arm is declared superior and becomes the
new control.

```
setup_trial_binom(
arms = c("A", "B", "C", "D"),
control = "A",
true_ys = c(0.2, 0.22, 0.24, 0.18),
data_looks = seq(from = 100, to = 1000, by = 100),
# Square-root-transformation-based control arm allocation for the initial
# control only and initial equal allocation to the non-control arms, followed
# by response-adaptive randomisation
control_prob_fixed = "sqrt-based start",
# Restrict response-adaptive randomisation
# Minimum probabilities of 20% for all non-control arms
# - must be NA for the initial control arm with fixed allocation probability
min_probs = c(NA, 0.2, 0.2, 0.2),
# Maximum probabilities of 65% for all non-control arms
# - must be NA for the initial control arm with fixed allocation probability
max_probs = c(NA, 0.65, 0.65, 0.65),
soften_power = 0.75
)#> Trial specification: generic binomially distributed outcome trial
#> * Undesirable outcome
#> * Common control arm: A
#> * Control arm probability fixed at 0.366
#> * Best arm: D
#>
#> Arms, true outcomes, starting allocation probabilities
#> and allocation probability limits:
#> arms true_ys start_probs fixed_probs min_probs max_probs
#> A 0.20 0.366 0.366 NA NA
#> B 0.22 0.211 NA 0.2 0.65
#> C 0.24 0.211 NA 0.2 0.65
#> D 0.18 0.211 NA 0.2 0.65
#>
#> Maximum sample size: 1000
#> Maximum number of data looks: 10
#> Planned data looks after: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 patients have reached follow-up
#> Number of patients randomised at each look: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000
#>
#> Superiority threshold: 0.99 (all analyses)
#> Inferiority threshold: 0.01 (all analyses)
#> No equivalence threshold
#> No futility threshold
#> Soften power for all analyses: 0.75
```

- Restricted response-adaptive randomisation
- Control-arm allocation probability
*matched*to that of the highest non-control arm (with re-scaling as necessary) - Applies to both the initial and subsequent control arms

```
setup_trial_binom(
arms = c("A", "B", "C", "D"),
control = "A",
true_ys = c(0.2, 0.22, 0.24, 0.18),
data_looks = seq(from = 100, to = 1000, by = 100),
# Specify starting probabilities
# When "match" is specified below in control_prob_fixed, the initial control
# arm's initial allocation probability must match the highest initial
# non-control arm allocation probability
start_probs = c(0.3, 0.3, 0.2, 0.2),
control_prob_fixed = "match",
# Restrict response-adaptive randomisation
# - these are applied AFTER "matching" when calculating new allocation
# probabilities
# - min_probs must be NA for the initial control arm when using matching
min_probs = c(NA, 0.2, 0.2, 0.2),
soften_power = 0.7
)#> Trial specification: generic binomially distributed outcome trial
#> * Undesirable outcome
#> * Common control arm: A
#> * Control arm probability matched to best non-control arm
#> * Best arm: D
#>
#> Arms, true outcomes, starting allocation probabilities
#> and allocation probability limits:
#> arms true_ys start_probs fixed_probs min_probs max_probs
#> A 0.20 0.3 NA NA NA
#> B 0.22 0.3 NA 0.2 NA
#> C 0.24 0.2 NA 0.2 NA
#> D 0.18 0.2 NA 0.2 NA
#>
#> Maximum sample size: 1000
#> Maximum number of data looks: 10
#> Planned data looks after: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 patients have reached follow-up
#> Number of patients randomised at each look: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000
#>
#> Superiority threshold: 0.99 (all analyses)
#> Inferiority threshold: 0.01 (all analyses)
#> No equivalence threshold
#> No futility threshold
#> Soften power for all analyses: 0.7
```

This example uses the `randomised_at_looks`

argument to
specify follow-up and/or data collection lag. In real use cases, this
should usually be considered, as this may affect the relative
performance of different trial designs and the extent to which the
‘final’ results after all patients have reached follow-up and are
analysed may differ from the results from the adaptive analyses with
some randomised patients not included due to outcome data not being
available yet for these patients.

```
setup_trial_binom(
arms = c("A", "B", "C", "D"),
control = "A",
true_ys = c(0.2, 0.22, 0.24, 0.18),
# Analyses conducted every time 100 patients have follow-up data available
data_looks = seq(from = 100, to = 1000, by = 100),
# Specify the number of patients randomised at each look - in this case, 200
# more patients are randomised than the number of patients that
# have follow-up data available at each look
randomised_at_looks = seq(from = 300, to = 1200, by = 100)
)#> Trial specification: generic binomially distributed outcome trial
#> * Undesirable outcome
#> * Common control arm: A
#>
#> * Best arm: D
#>
#> Arms, true outcomes, starting allocation probabilities
#> and allocation probability limits:
#> arms true_ys start_probs fixed_probs min_probs max_probs
#> A 0.20 0.25 NA NA NA
#> B 0.22 0.25 NA NA NA
#> C 0.24 0.25 NA NA NA
#> D 0.18 0.25 NA NA NA
#>
#> Maximum sample size: 1200
#> Maximum number of data looks: 10
#> Planned data looks after: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 patients have reached follow-up
#> Number of patients randomised at each look: 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200
#>
#> Superiority threshold: 0.99 (all analyses)
#> Inferiority threshold: 0.01 (all analyses)
#> No equivalence threshold
#> No futility threshold
#> Soften power for all analyses: 1 (no softening)
```

In this example, we specify different probability thresholds for
superiority and inferiority stopping rules at different adaptive
analyses. Varying probability thresholds may similarly be specified for
stopping rules for equivalence and futility. Importantly, all
probability thresholds must be specified such that each subsequent
threshold is never stricter than the previous threshold. Varying
thresholds may also be used to make some stopping rules first function
at later analyses (e.g., as long as the stopping threshold for
`superiority`

is `1`

and the stopping threshold
for `inferiority`

is `0`

, trials will not be
stopped and arms will not be dropped due to these rules).

```
setup_trial_binom(
arms = c("A", "B", "C", "D"),
control = "A",
true_ys = c(0.2, 0.22, 0.24, 0.18),
# Analyses conducted every time 100 patients have follow-up data available
data_looks = seq(from = 100, to = 1000, by = 100),
# Specify varying inferiority/superiority thresholds
# When specifying varying thresholds, the number of thresholds must match
# the number of analyses, and thresholds may never be stricter than the
# threshold used in the previous analysis
# Superiority threshold decreasing from 0.99 to 0.95 during the first five
# analyses, and remains stationary at 0.95 after that
superiority = c(seq(from = 0.99, to = 0.95, by = -0.01), rep(0.95, 5)),
# Similarly for inferiority thresholds, but in the opposite direction
inferiority = c(seq(from = 0.01, to = 0.05, by = 0.01), rep(0.05, 5)),
)#> Trial specification: generic binomially distributed outcome trial
#> * Undesirable outcome
#> * Common control arm: A
#>
#> * Best arm: D
#>
#> Arms, true outcomes, starting allocation probabilities
#> and allocation probability limits:
#> arms true_ys start_probs fixed_probs min_probs max_probs
#> A 0.20 0.25 NA NA NA
#> B 0.22 0.25 NA NA NA
#> C 0.24 0.25 NA NA NA
#> D 0.18 0.25 NA NA NA
#>
#> Maximum sample size: 1000
#> Maximum number of data looks: 10
#> Planned data looks after: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 patients have reached follow-up
#> Number of patients randomised at each look: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000
#>
#> Superiority thresholds:
#> 0.99, 0.98, 0.97, 0.96, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95
#> Inferiority thresholds:
#> 0.01, 0.02, 0.03, 0.04, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05
#> No equivalence threshold
#> No futility threshold
#> Soften power for all analyses: 1 (no softening)
```

In this example, a trial design with four arms and restricted RAR (minimum allocation limits) is specified, with additional specification that the minimum allocation limits should be rescaled proportionally when arms are dropped (rescaling can similarly be applied to fixed allocation probabilities):

```
setup_trial_binom(
arms = c("A", "B", "C", "D"),
control = "A",
true_ys = c(0.2, 0.2, 0.2, 0.2),
min_probs = rep(0.15, 4), # Specify initial minimum allocation probabilities
# Rescale allocation probability limits as arms are dropped
rescale_probs = "limits",
data_looks = seq(from = 100, to = 1000, by = 100)
)#> Trial specification: generic binomially distributed outcome trial
#> * Undesirable outcome
#> * Common control arm: A
#>
#> * Best arms: A and B and C and D
#>
#> Arms, true outcomes, starting allocation probabilities
#> and allocation probability limits (min/max_probs rescaled):
#> arms true_ys start_probs fixed_probs min_probs max_probs
#> A 0.2 0.25 NA 0.15 NA
#> B 0.2 0.25 NA 0.15 NA
#> C 0.2 0.25 NA 0.15 NA
#> D 0.2 0.25 NA 0.15 NA
#>
#> Maximum sample size: 1000
#> Maximum number of data looks: 10
#> Planned data looks after: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 patients have reached follow-up
#> Number of patients randomised at each look: 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000
#>
#> Superiority threshold: 0.99 (all analyses)
#> Inferiority threshold: 0.01 (all analyses)
#> No equivalence threshold
#> No futility threshold
#> Soften power for all analyses: 1 (no softening)
```