Build a fahb analysis object

Given a fahb_problem object calculate summary statistics which can inform progression decisions. These include both standard progression criteria statistics, and the expectation of the posterior predictive distribution of the time until the trial recruits.

Usage

fahb_analysis(n_pilot, t_pilot, problem, site_t = NULL, bayes_model = NULL)

Arguments

n_pilot

integer vector of numbers recruited at each open site.

t_pilot

numeric vector of time (in years) each site has been open.

problem

object of class fahb_problem.

site_t

In the case of an external pilot, the time taken for all pilot sites to open.

bayes_model

optional object of class brmsfit which will be used in the Bayesian analysis via brms::update() to avoid compiling a new model.

Value

An object of class fahb_analysis.

Examples

## Example illustrating a full analysis workflow
## (Not run on CRAN due to Bayesian model fitting)

# \donttest{
problem <- fahb_problem()
problem <- forecast(problem)

## Pilot trial data
n_pilot <- c(3, 5, 2)
t_pilot <- c(0.5, 0.6, 0.4)

analysis <- fahb_analysis(
  n_pilot = n_pilot,
  t_pilot = t_pilot,
  problem = problem
)
#> Compiling the model...
#> the number of chains is less than 1; sampling not done

print(analysis)
#> Standard progression criteria statistics:
#>       n_p       m_p       r_p 
#> 10.000000  3.000000  6.666667 
#> 
#> Expected posterior predictive time to recruit:
#> exp_pp_T 
#> 3.538887 
#> 
#> Posterior predictive distribution quantiles:
#>     0.5%     2.5%      20%      50%      80%    97.5%    99.5% 
#> 2.258355 2.531464 3.018079 3.475001 4.014829 4.917012 5.566843 
#> 
#> Posterior site opening rate hyperparamaters (Gamma):
#> shape  rate 
#> 33.00  3.35 
#> 
plot(analysis)
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# }