getGoDecisions {bhmbasket}  R Documentation 
getGoDecisions
Description
This function applies decision rules to the analyzed trials.
The resulting decision_list
can be further processed with
getGoProbabilities
or
continueRecruitment
.
Usage
getGoDecisions(
analyses_list,
cohort_names,
evidence_levels,
boundary_rules,
overall_min_gos = 1
)
Arguments
analyses_list 
An object of class 
cohort_names 
A vector of strings with the names of the cohorts, e.g.

evidence_levels 
A vector of numerics in 
boundary_rules 
A quote of a vector for the boundary rules,

overall_min_gos 
A positive integer for the minimum number of
cohortwise go decisions required for an overall go decision
Default: 
Details
This function applies decision rules of the following type to the outcomes of (simulated) basket trials with binary endpoints:
P(p_jdata > p_{B,j}) > \gamma,
where p_jdata
is the posterior response rate of cohort j
,
p_{B,j}
is the response rate boundary of cohort j
,
and \gamma
is the evidence level.
This rule can equivalently be written as
q_{1\gamma,j} > p_{B,j},
where q_{1\gamma,j}
is the 1\gamma
quantile of the posterior
response rate of cohort j
.
The arguments cohort_names
and evidence_levels
determine
q_{1\gamma,j}
, where the entries of cohort_names
and
evidence_levels
are matched corresponding to their order.
The argument boundary_rules
provides the rules that describe what
should happen with the posterior quantiles q_{1\gamma,j}
.
The first posterior quantile determined by the first items of
cohort_names
and evidence_levels
is referred to as x[1]
,
the second as x[2]
, etc.
Using the quote(c(...))
notation,
many different rules can be implemented.
A decision rule for only one cohort would be
boundary_rules = quote(c(x[1] > 0.1))
,
cohort_names = 'p_1'
, and evidence_levels = 0.5
,
which implements the rule P(p_1data > 0.1) > 0.5
.
The number of decisions to be taken must match the number of cohorts, i.e.
for each cohort there must be a decision rule in the vector separated by a comma.
See the example section for a decision rule for more than one cohort and
the example of negateGoDecisions
for the implementation of a more complex decision rule.
Value
An object of class decision_list
Author(s)
Stephan Wojciekowski
See Also
performAnalyses
getGoProbabilities
negateGoDecisions
continueRecruitment
Examples
scenarios_list < simulateScenarios(
n_subjects_list = list(c(10, 20, 30)),
response_rates_list = list(c(0.1, 0.1, 0.9)),
n_trials = 10)
analyses_list < performAnalyses(
scenario_list = scenarios_list,
target_rates = rep(0.5, 3),
n_mcmc_iterations = 100)
## Decision rule for more than one cohort
decisions_list < getGoDecisions(
analyses_list = analyses_list,
cohort_names = c("p_1", "p_2", "p_3"),
evidence_levels = c(0.5, 0.5, 0.8),
boundary_rules = quote(c(x[1] > 0.7, x[2] < 0.3, x[3] < 0.6)))
## Decision rule for only two of the three cohorts
decisions_list < getGoDecisions(
analyses_list = analyses_list,
cohort_names = c("p_1", "p_3"),
evidence_levels = c(0.5, 0.8),
boundary_rules = quote(c(x[1] > 0.7, TRUE, x[3] < 0.6)),
overall_min_gos = 2L)
## Different decision rules for each method
## This works the same way for the different evidence_levels
decisions_list < getGoDecisions(
analyses_list = analyses_list,
cohort_names = c("p_1", "p_2", "p_3"),
evidence_levels = c(0.5, 0.5, 0.8),
boundary_rules = list(quote(c(x[1] > 0.1, x[2] < 0.5, x[3] < 0.1)), # "berry"
quote(c(x[1] > 0.2, x[2] < 0.4, x[3] < 0.2)), # "exnex"
quote(c(x[1] > 0.3, x[2] < 0.3, x[3] < 0.3)), # "exnex_adj"
quote(c(x[1] > 0.4, x[2] < 0.2, x[3] < 0.4)), # "pooled"
quote(c(x[1] > 0.5, x[2] < 0.1, x[3] < 0.5)))) # "stratified"