bhmbasket (version 1.0.0)

negateGoDecisions: negateGoDecisions

Description

Negates the go decisions derived with getGoDecisions.

Usage

negateGoDecisions(go_decisions_list, overall_min_nogos = "all")

Value

A list of NoGo decisions of class decision_list

Arguments

go_decisions_list

An object of class decision_list, as returned by getGoDecisions

overall_min_nogos

Either a non-negative integer or the string all for the minimum number of cohort-level NoGo decisions required for an overall NoGo decision, Default: all

Author

Stephan Wojciekowski

Details

This function is intended for implementing decision rules with a consider zone as e.g. proposed in "Bayesian design of proof-of-concept trials" by Fisch et al. (2015). This approach involves two criteria, Significance and Relevance.

• Significance: high evidence that the treatment effect is greater than some smaller value (e.g. treatment effect under H0)

• Relevance: moderate evidence that the treatment effect is greater than some larger value (e.g. treatment effect under a certain alternative)

The decision for a cohort is then taken as follows:

• Go decision: Significance and Relevance

• Consider decision: either Significance, or Relevance, but not both

• NoGo decision: no Significance and no Relevance

In the example below, the following criteria for are implemented for each of the three cohorts:

• Significance: \(P(p_j > 0.4) > 0.95\)

• Relevance: \(P(p_j > 0.8) > 0.5\)

References

Fisch, Roland, et al. "Bayesian design of proof-of-concept trials." Therapeutic innovation & regulatory science 49.1 (2015): 155-162.

See Also

getGoDecisions

Examples

scenarios_list <- simulateScenarios(
  n_subjects_list     = list(c(10, 20, 30)),
  response_rates_list = list(rep(0.9, 3)),
  n_trials            = 10)

analysis_list <- performAnalyses(
  scenario_list      = scenarios_list,
  target_rates       = rep(0.5, 3),
  n_mcmc_iterations  = 100)

go_decisions_list <- getGoDecisions(
  analyses_list   = analysis_list,
  cohort_names    = c("p_1", "p_2", "p_3",
                      "p_1", "p_2", "p_3"),
  evidence_levels = c(0.5,  0.5,  0.5,
                      0.95, 0.95, 0.95),
  boundary_rules  = quote(c(x[1] > 0.8 & x[4] > 0.4,
                            x[2] > 0.8 & x[5] > 0.4,
                            x[3] > 0.8 & x[6] > 0.4)))

nogo_decisions <- negateGoDecisions(getGoDecisions(
  analyses_list   = analysis_list,
  cohort_names    = c("p_1", "p_2", "p_3",
                      "p_1", "p_2", "p_3"),
  evidence_levels = c(0.5,  0.5,  0.5,
                      0.95, 0.95, 0.95),
  boundary_rules  = quote(c(x[1] > 0.8 | x[4] > 0.4,
                            x[2] > 0.8 | x[5] > 0.4,
                            x[3] > 0.8 | x[6] > 0.4))))

getGoProbabilities(go_decisions_list, nogo_decisions)