EurosarcBayes (version 1.0)

bayes_binom_two_postprob: Bayesian, single arm, two endpoint trial design, using posterior probability to make decisions.

Description

Computes the decision rules for a single arm, two endpoint bayesian trial using posterior probabilities to generate the decision rules. This program assumes that the two endpoints are independent.

Usage

bayes_binom_two_postprob(t, r, reviews, pra, prb, pta, ptb,
futility_critical_value, futility_prob_stop, efficacy_critical_value,
efficacy_prob_stop, toxicity_critical_value, toxicity_prob_stop,
no_toxicity_critical_value, no_toxicity_prob_stop)

Arguments

t,r

A vector of the probability of response and toxicity for the simulation scenarios used to compute frequentist properties. The print function requires the first to be the alternative hypothesis and subsequent entries to be the three null hypotheses. This can be run with any scenario when not using the print method

reviews

A vector of the number of patients each interim and final analysis will occur at

pra,prb,pta,ptb

Numeric values for the beta prior distribution to be used

futility_critical_value, efficacy_critical_value, toxicity_critical_value, no_toxicity_critical_value

Four values, for the critical values to be used as thresholds for the posterior distribution

futility_prob_stop, efficacy_prob_stop, toxicity_prob_stop, no_toxicity_prob_stop

Values or vectors of the probability required to stop at this interim analysis. If you do not wish to stop due to a rule set this to 1 at that analysis. If you wish to ignor a rule when stopping set this to 0 at that analysis

Value

Returns an object of class '>trialDesign_binom_two

Details

Returns an object of S4 class trialDesign_binom_two-class. This has plot and print methods. For comparison between designs saved as trialDesign_binom_two objects there is a print function for the S3 class list_trialDesign_binom_two.

See Also

bayes_binom_two_postprob, bayes_binom_two_postlike,bayes_binom_two_loss

Examples

# NOT RUN {
# modelled toxicity probability
t=c(0.1,0.1,0.3,0.3)
# modelled response probability
r=c(0.35,0.2,0.2,0.35)

reviews=c(10,15,20,25,30,35,40)

# uniform prior
pra=1;prb=1;pta=1;ptb=1

futility_critical_value=0.35
futility_prob_stop=c(0.95,0.95,0.95,0.95,0.95,0.95,0)

efficacy_critical_value=0.2
efficacy_prob_stop=c(1,1,0.95,0.95,0.95,0.95,0.9)

toxicity_critical_value=0.1
toxicity_prob_stop=c(0.95,0.95,0.95,0.95,0.95,0.95,0.95)

no_toxicity_critical_value=0.3
toxicity_prob_stop=c(0.95,0.95,0.95,0.95,0.95,0.95,0.95)

s=bayes_binom_two_postprob(t,r,reviews,pra,prb,pta,ptb,
futility_critical_value,futility_prob_stop,efficacy_critical_value,
efficacy_prob_stop,toxicity_critical_value,toxicity_prob_stop,
no_toxicity_critical_value,toxicity_prob_stop)

s

plot(s)
# }