peps2_process {trialr}

Process RStan samples from a BEBOP model fit to PePS2 data

Description

Process RStan samples from a BEBOP model fit to PePS2 data. This step lets us make inferences about whether the modelled efficacy and toxicity probabilities suggest the treatment is acceptable in each of the cohorts under study. The parameters have default values to match those used in the PePS2 trial. See the accompanying vignette for a full description.

Usage

peps2_process(
  fit,
  min_eff = 0.1,
  max_tox = 0.3,
  eff_cert = 0.7,
  tox_cert = 0.9
)

Arguments

fit

An instance of rstan::stanmodel, derived by fitting data to the BEBOP in PePS2 model. Use stan_peps2.

min_eff

The lower efficacy probability threshold; a number between 0 and 1.

max_tox

The upper toxicity probability threshold; a number between 0 and 1.

eff_cert

Certainty required to infer the treatment is acceptable with regards to being probably efficacious; a number between 0 and 1.

tox_cert

Certainty required to infer the treatment is acceptable with regards to being probably tolerable; a number between 0 and 1.

Value

a list with the following items:

• ProbEff, the posterior mean probability of efficacy in the 6 cohorts.

• ProbAccEff, the posterior mean probability that the probability of efficacy exceeds min_eff, in the 6 cohorts.

• ProbTox, the posterior mean probability of toxicity in the 6 cohorts.

• ProbAccTox, the posterior mean probability that the probability of toxicity is less than max_tox, in the 6 cohorts.

• Accept, a vector of logical values to show whether treatment should be accepted in the 6 cohorts. Treatment is acceptable when it is probably efficacious and probably not toxic, with respect to the described rules.

• alpha, the posterior mean estimate of alpha.

• beta, the posterior mean estimate of beta.

• gamma, the posterior mean estimate of gamma.

• zeta, the posterior mean estimate of zeta.

• lambda, the posterior mean estimate of lambda.

• psi, the posterior mean estimate of psi.

See Also

peps2_get_data

Examples

set.seed(123)
fit <- stan_peps2(
  eff = c(0, 1, 0, 1, 0, 0),
  tox = c(0, 0, 1, 1, 0, 0),
  cohorts = c(3, 1, 1, 4, 5, 6)
)
decision <- peps2_process(fit)
decision$Accept
decision$ProbEff
decision$ProbAccEff