BACCT (version 1.0)

decision_eval: Evaluating a Decision Rule

Description

Applies a decision rule to a "BAC" class object and provides rule evaluation

Usage

decision_eval(object, decision.rule, control.range, es, csv.name = NULL)

Arguments

object

An object of class "BAC".

decision.rule

A vector of c(a,b) specifying the thresholds for claiming significance (or probabilities of making correct go/no-go decisions at interim look). See "Details".

control.range

A vector of control rates at which the decision rule is evaluated.

es

A vector of treatment arm effect sizes, compared to control arm.

csv.name

If a name is specified, the output data set is exported in CSV format.

Value

An object of class "BACdecision".

Details

The decision rules specified in c(a,b) may be in the context of either interim or final analysis. At the interim, a "go" decision is made if the criterion in the "BAC" object exceeds b and a "no go" decision if such criterion is below a. Otherwise, the decision falls in the gray zone.

For the final analysis, the decision rule should satisfy a=b. Significance is claimed if the criterion in the "BAC" object exceeds a. Specifying an a larger than b will lead to an error.

For interim analysis, specified decision rule is evaluated by the probability of making a correct go or no go decision. For final analysis, power or type-I error is computed.

Negative es values are allowed if a lower rate is desirable.

Examples

## Not run: 
# #borrow from 3 historical trials#
# yh = c(11,300,52);nh = c(45,877,128)
# #specify current trial sample sizes#
# n1 = 20;n2 = 30
# obj = BAC_binom(yh=yh,nh=nh,n1=n1,n2=n2,n.chain=5,
# criterion.type="prob",prob.threshold=0.1,sim.mode="express")
# 
# rule = decision_eval(obj,decision.rule=c(0.05,0.15),
# control.range=seq(0.3,0.5,0.01),es=c(0,0.1,0.2),csv.name="result.csv")
# 
# #S3 method for class "BACdecision"
# plot(rule,interim=T)
# ## End(Not run)