freq_binom_two_bryantday_twostage {EurosarcBayes} | R Documentation |
Single arm, two independent endpoint extension to Simons two-stage design
Description
This function searches for solutions to a single arm two-stage two-endpoint trial first proposed by Bryant and Day (1995). The two endpoints are assumed independent. A wrapper function to compute the Bayesian properties is also provided.
Usage
freq_binom_two_bryantday_twostage(r0=0.2, r1=0.35, t0=0.3, t1=0.1,
alpha.r, power, nrange, alpha.t=alpha.r)
Arguments
r0 , r1 |
Probability of success under H0 and H1 |
t0 , t1 |
Probability of toxicity under H0 and H1 |
alpha.r |
Probability of a false positive trial if the response H0 is true and toxicity is either H0 or H1 |
alpha.t |
Probability of a false positive trial if the toxicity H0 is true and response is either H0 or H1 |
power |
Probability of true positive trial result assuming H1 is true |
nrange |
A vector of the total number of patients to recruit up to each stage of the trial |
Value
Returns an object of class binom_two_bryantday
. This can be transformed into an object of class trialDesign_binom_two
using properties (see properties
) and supplying the necessary values.
References
Simon R. Optimal two-stage designs for phase II clinical trials. Control Clin Trials 1989; 10: 1-10.
Bryant J, Day R. Incorporating toxicity considerations into the design of two-stage phase II clinical trials. Biometrics 1995; 51: 1372-1383.
Examples
r1=0.3
r0=0.1
t0=0.3
t1=0.1
power=0.8
alpha=0.1
###############################################################
# Bryant and Day, two stage
nrange=20:27
out=freq_binom_two_bryantday_twostage(r0,r1,t0,t1,alpha,power,nrange)
###############################################################
## Frequentist simulations
# modelled toxicity probability
t=c(0.1,0.3,0.1,0.3)
# modelled response probability
r=c(0.3,0.1,0.1,0.3)
## Bayesian uniform prior
pra=1;prb=1;pta=1;ptb=1
## bayesian cutoffs
futility_critical_value=0.3
efficacy_critical_value=0.1
toxicity_critical_value=0.1
no_toxicity_critical_value=0.3
byrant_day_optimal=properties(out,t,r,pra,prb,pta,ptb,
futility_critical_value,efficacy_critical_value,
toxicity_critical_value,no_toxicity_critical_value,
target="optimal")
byrant_day_minmax=properties(out,t,r,pra,prb,pta,ptb,
futility_critical_value,efficacy_critical_value,
toxicity_critical_value,no_toxicity_critical_value,
target="minmax")
bayes_table=list(byrant_day_optimal=byrant_day_optimal,
byrant_day_minmax=byrant_day_minmax)
class(bayes_table)=c("list_trialDesign_binom_two",class(bayes_table))
bayes_table