negateGoDecisions {bhmbasket} | R Documentation |

## negateGoDecisions

### Description

Negates the go decisions derived with
`getGoDecisions`

.

### Usage

```
negateGoDecisions(go_decisions_list, overall_min_nogos = "all")
```

### Arguments

`go_decisions_list` |
An object of class |

`overall_min_nogos` |
Either a non-negative integer or the string |

### 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`

### Value

A list of NoGo decisions of class `decision_list`

### Author(s)

Stephan Wojciekowski

### References

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

### See Also

### 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)
```

*bhmbasket*version 0.9.5 Index]