adaptDiag (version 0.1.0)

summarise_trials: Summarise results of multiple simulated trials to give the operating characteristics

Description

Summarise results of multiple simulated trials to give the operating characteristics

Usage

summarise_trials(data, min_pos = 1, fut = 0)

Arguments

data

list. Output from the multi_trial function.

min_pos

integer. The minimum number of reference positive cases before stopping is allowed. Default is min_pos = 1.

fut

scalar. A probability threshold at which the posterior predictive probability of eventual success is compared to. If the probability is less than fut, the trial stops for binding futility. Default is fut = 0, which corresponds to no stopping for futility.

Value

A data frame of row length 1, with the following columns:

• power: Power is defined as the proportion of trials that result in success, irrespective of whether it is an early stop for success or not. Trials that stop for futility, but which subsequently go on to be successful, are not considered as a success. In other words, the futility decision is binding, and in practice, if a trial triggered a futility rule, the sponsor would not see the eventual outcome if the trial were to continue enrolling. When the performance goals are set equal to the respective true values, the power returned is the type I error.

• stop_futility: The proportion of trials that stopped early for expected futility.

• n_avg: The average sample size for trials at the stage they stopped.

• sens: The average sensitivity for trials at the stage they stopped.

• spec: The average specificity for trials at the stage they stopped.

• mean_pos: The average number of reference positive cases for trials at the stage they stopped.

Examples

# NOT RUN {
data <- multi_trial(
    sens_true = 0.9,
    spec_true = 0.95,
    prev_true = 0.1,
    endpoint = "both",
    sens_pg = 0.8,
    spec_pg = 0.8,
    prior_sens = c(1, 1),
    prior_spec = c(1, 1),
    prior_prev = c(1, 1),
    succ_sens = 0.95,
    succ_spec = 0.95,
    n_at_looks = c(200, 400, 600, 800, 1000),
    n_mc = 10000,
    n_trials = 20,
    ncores = 1
    )

summarise_trials(data, fut = 0.05, min_pos = 10)
# }