Last data update: 2014.03.03
R: Bayesian, single arm, two endpoint trial design, using...
print.list_trialDesign_binom_two R Documentation
Bayesian, single arm, two endpoint trial design, using posterior probability to make decisions.
Description
This class is used to compare designs and methodologies frequentist and bayesian properties. To use it create a list of trial designs of class trialDesign_binom_two and assign the class as list_trialDesign_binom_two (class(x)=c("list_trialDesign_binom_two",class(x))).
Usage
## S3 method for class 'list_trialDesign_binom_two'
print(x, ...)
Arguments
x
A list of the S4 class object bayes_binom_two_postprob
...
Standard arguments to pass to print
See Also
bayes_binom_two_postprob, bayes_binom_two_postlike,bayes_binom_two_loss,freq_binom_two_bryantday_twostage
Examples
## Frequentist simulations
# modelled toxicity probability
t=c(0.1,0.3,0.1,0.3)
# modelled response probability
r=c(0.35,0.2,0.2,0.35)
## Bayesian uniform prior
pra=1;prb=1;pta=1;ptb=1
## bayesian cutoffs
futility_critical_value=0.35
efficacy_critical_value=0.2
toxicity_critical_value=0.1
no_toxicity_critical_value=0.3
###############################################################
# Frequentist methods
###############################################################
# Single stage
r1=0.35
r0=0.2
t0=0.3
t1=0.1
power=0.8
alpha=0.1
nmax=50
out_single=freq_binom_two_singlestage(r0,r1,t0,t1,power,alpha,nmax,
adjust=TRUE)
single_stage=properties(out_single,t,r,pra,prb,pta,ptb,
futility_critical_value,efficacy_critical_value,
toxicity_critical_value,no_toxicity_critical_value)
print(single_stage)
###############################################################
# Bayesian posterior probability approach
# analysis at
reviews=c(44)
# Stopping rules at each analysis
futility_prob_stop=0.9
efficacy_prob_stop=0.9
toxicity_prob_stop=0.9
no_toxicity_prob_stop=0.9
bayes_prob_single=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)
bayes_prob_single
# analysis at
reviews=c(10,17,24,30,37,44)
# Stopping rules at each analysis
futility_prob_stop=c(0.95,0.95,0.95,0.95,0.95,0.9)
efficacy_prob_stop=c(1,1,0.95,0.95,0.95,0.9)
toxicity_prob_stop=c(0.95,0.95,0.95,0.95,0.95,0.9)
no_toxicity_prob_stop=c(1,1,0.95,0.95,0.95,0.9)
bayes_prob_six=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)
plot(bayes_prob_six)
###############################################################
# Bayesian posterior likelihood approach
###############################################################
reviews=c(11,17,24,30,37,44)
efficacy_prob_stop=0.9
toxicity_prob_stop=0.9
# interim required probability to stop
int_combined_prob=0.95
int_futility_prob=1
int_toxicity_prob=1
int_efficacy_prob=0.95
bayes_like_six=bayes_binom_two_postlike(t,r,reviews,pra,prb,pta,
ptb,efficacy_critical_value,efficacy_prob_stop,
toxicity_critical_value,toxicity_prob_stop,int_combined_prob,
int_futility_prob,int_toxicity_prob,int_efficacy_prob,
futility_critical_value,no_toxicity_critical_value)
plot(bayes_like_six)
###############################################################
## Table of all designs
###############################################################
tble=list(single_stage=single_stage,bayes_prob_single=bayes_prob_single,
bayes_prob_six=bayes_prob_six,bayes_like_six=bayes_like_six)
class(tble)=c("list_trialDesign_binom_two",class(tble))
tble
###############################################################
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(EurosarcBayes)
Loading required package: shiny
Loading required package: VGAM
Loading required package: stats4
Loading required package: splines
Loading required package: data.table
Loading required package: plyr
Loading required package: clinfun
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/EurosarcBayes/print.list_trialDesign_binom_two.Rd_%03d_medium.png", width=480, height=480)
> ### Name: print.list_trialDesign_binom_two
> ### Title: Bayesian, single arm, two endpoint trial design, using posterior
> ### probability to make decisions.
> ### Aliases: print.list_trialDesign_binom_two list_trialDesign_binom_two
>
> ### ** Examples
>
> ## Frequentist simulations
> # modelled toxicity probability
> t=c(0.1,0.3,0.1,0.3)
> # modelled response probability
> r=c(0.35,0.2,0.2,0.35)
>
> ## Bayesian uniform prior
> pra=1;prb=1;pta=1;ptb=1
> ## bayesian cutoffs
> futility_critical_value=0.35
> efficacy_critical_value=0.2
> toxicity_critical_value=0.1
> no_toxicity_critical_value=0.3
>
> ###############################################################
> # Frequentist methods
> ###############################################################
> # Single stage
>
> r1=0.35
> r0=0.2
> t0=0.3
> t1=0.1
>
> power=0.8
> alpha=0.1
>
> nmax=50
> out_single=freq_binom_two_singlestage(r0,r1,t0,t1,power,alpha,nmax,
+ adjust=TRUE)
>
> single_stage=properties(out_single,t,r,pra,prb,pta,ptb,
+ futility_critical_value,efficacy_critical_value,
+ toxicity_critical_value,no_toxicity_critical_value)
cut-points at each analysis
patient review low toxicity high toxicity poor outcome good outcome
1 44 9 10 12 13
Frequentist properties of design
Stopping rules T=0.1, R=0.35 T=0.3, R=0.2
1 Stop early - Futility/Toxicity 0.00 0.00
4 Continue to final analysis - Futility/Toxicity 18.88 99.06
2 Stop early - Efficacy 0.00 0.00
3 Continue to final analysis - Efficacy 81.12 0.94
6 Expected number of patients recruited 44.00 44.00
T=0.1, R=0.2 T=0.3, R=0.35
1 0.00 0.00
4 91.51 91.05
2 0.00 0.00
3 8.49 8.95
6 44.00 44.00
Bayesian properties of trial design
n T>0.3 T>0.1 T>0.3 T>0.1 R>0.2 R>0.35 R>0.2 R>0.35
44 0.093 0.988 0.165 0.996 0.901 0.155 0.948 0.244
Futility P(R<0.35)=0.845
Efficacy P(R>0.2)=0.948
Toxicity ok P(T<0.3)=0.907
Toxicity P(T>0.1)=0.996>
> print(single_stage)
n alpha beta Exp.P0 Exp.P1 post.futility post.efficacy post.toxicity
44 0.0895 0.1888 44 44 0.845 0.948 0.996
post.no_toxicity
0.907
>
> ###############################################################
> # Bayesian posterior probability approach
>
>
> # analysis at
> reviews=c(44)
> # Stopping rules at each analysis
> futility_prob_stop=0.9
> efficacy_prob_stop=0.9
> toxicity_prob_stop=0.9
> no_toxicity_prob_stop=0.9
>
> bayes_prob_single=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)
cut-points at each analysis
patient review low toxicity high toxicity poor outcome good outcome
1 44 9 10 11 12
Frequentist properties of design
Stopping rules T=0.1, R=0.35 T=0.3, R=0.2
1 Stop early - Futility/Toxicity 0.00 0.00
4 Continue to final analysis - Futility/Toxicity 11.61 98.32
2 Stop early - Efficacy 0.00 0.00
3 Continue to final analysis - Efficacy 88.39 1.68
6 Expected number of patients recruited 44.00 44.00
T=0.1, R=0.2 T=0.3, R=0.35
1 0.00 0.00
4 84.74 90.25
2 0.00 0.00
3 15.26 9.75
6 44.00 44.00
Bayesian properties of trial design
n T>0.3 T>0.1 T>0.3 T>0.1 R>0.2 R>0.35 R>0.2 R>0.35
44 0.093 0.988 0.165 0.996 0.826 0.09 0.901 0.155
Futility P(R<0.35)=0.91
Efficacy P(R>0.2)=0.901
Toxicity ok P(T<0.3)=0.907
Toxicity P(T>0.1)=0.996>
> bayes_prob_single
n alpha beta Exp.P0 Exp.P1 post.futility post.efficacy post.toxicity
44 0.1526 0.1161 44 44 0.91 0.901 0.996
post.no_toxicity
0.907
>
>
> # analysis at
> reviews=c(10,17,24,30,37,44)
> # Stopping rules at each analysis
> futility_prob_stop=c(0.95,0.95,0.95,0.95,0.95,0.9)
> efficacy_prob_stop=c(1,1,0.95,0.95,0.95,0.9)
> toxicity_prob_stop=c(0.95,0.95,0.95,0.95,0.95,0.9)
> no_toxicity_prob_stop=c(1,1,0.95,0.95,0.95,0.9)
>
> bayes_prob_six=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)
cut-points at each analysis
patient review low toxicity high toxicity poor outcome good outcome
1 10 NA 3 0 NA
2 17 NA 4 2 NA
3 24 3 5 4 8
4 30 4 6 6 10
5 37 6 7 8 12
6 44 9 10 11 12
Frequentist properties of design
Stopping rules T=0.1, R=0.35 T=0.3, R=0.2
1 Stop early - Futility/Toxicity 24.07 99.02
4 Continue to final analysis - Futility/Toxicity 2.73 0.31
2 Stop early - Efficacy 64.32 0.52
3 Continue to final analysis - Efficacy 8.88 0.16
6 Expected number of patients recruited 26.46 13.62
T=0.1, R=0.2 T=0.3, R=0.35
1 77.92 95.90
4 8.44 0.09
2 9.10 3.78
3 4.54 0.23
6 24.62 14.36
Bayesian properties of trial design
n T>0.3 T>0.1 T>0.3 T>0.1 R>0.2 R>0.35 R>0.2 R>0.35
10 NA NA 0.570 0.981 0.086 0.009 NA NA
17 NA NA 0.333 0.972 0.271 0.024 NA NA
24 0.033 0.764 0.193 0.967 0.421 0.032 0.953 0.467
30 0.024 0.807 0.135 0.969 0.571 0.046 0.967 0.455
37 0.036 0.920 0.079 0.968 0.655 0.047 0.971 0.399
44 0.093 0.988 0.165 0.996 0.826 0.090 0.901 0.155
Futility P(R<0.35)=0.91
Efficacy P(R>0.2)=0.901
Toxicity ok P(T<0.3)=0.907
Toxicity P(T>0.1)=0.967>
> plot(bayes_prob_six)
>
>
> ###############################################################
> # Bayesian posterior likelihood approach
> ###############################################################
> reviews=c(11,17,24,30,37,44)
>
> efficacy_prob_stop=0.9
> toxicity_prob_stop=0.9
>
> # interim required probability to stop
> int_combined_prob=0.95
> int_futility_prob=1
> int_toxicity_prob=1
> int_efficacy_prob=0.95
>
> bayes_like_six=bayes_binom_two_postlike(t,r,reviews,pra,prb,pta,
+ ptb,efficacy_critical_value,efficacy_prob_stop,
+ toxicity_critical_value,toxicity_prob_stop,int_combined_prob,
+ int_futility_prob,int_toxicity_prob,int_efficacy_prob,
+ futility_critical_value,no_toxicity_critical_value)
cut-points at each analysis
patient review low toxicity high toxicity poor outcome good outcome
1 11 0 4 0 7
2 17 0 6 1 7
3 24 2 7 3 9
4 30 3 8 5 10
5 37 5 9 7 12
6 44 9 10 11 12
Frequentist properties of design
Stopping rules T=0.1, R=0.35 T=0.3, R=0.2
1 Stop early - Futility/Toxicity 9.98 95.64
4 Continue to final analysis - Futility/Toxicity 6.07 2.63
2 Stop early - Efficacy 62.49 0.26
3 Continue to final analysis - Efficacy 21.45 1.47
6 Expected number of patients recruited 32.00 16.80
T=0.1, R=0.2 T=0.3, R=0.35
1 62.59 87.45
4 22.86 4.90
2 6.57 1.83
3 7.98 5.82
6 29.53 21.03
Bayesian properties of trial design
n T>0.3 T>0.1 T>0.3 T>0.1 R>0.2 R>0.35 R>0.2 R>0.35
11 0.014 0.282 0.724 0.996 0.069 0.006 0.999 0.974
17 0.002 0.150 0.722 0.999 0.099 0.005 0.984 0.728
24 0.009 0.537 0.512 0.998 0.234 0.010 0.983 0.630
30 0.007 0.624 0.386 0.997 0.393 0.018 0.967 0.455
37 0.014 0.825 0.255 0.997 0.500 0.020 0.971 0.399
44 0.093 0.988 0.165 0.996 0.826 0.090 0.901 0.155
Futility P(R<0.35)=0.91
Efficacy P(R>0.2)=0.901
Toxicity ok P(T<0.3)=0.907
Toxicity P(T>0.1)=0.996>
> plot(bayes_like_six)
>
> ###############################################################
> ## Table of all designs
> ###############################################################
> tble=list(single_stage=single_stage,bayes_prob_single=bayes_prob_single,
+ bayes_prob_six=bayes_prob_six,bayes_like_six=bayes_like_six)
>
> class(tble)=c("list_trialDesign_binom_two",class(tble))
> tble
name n alpha beta Exp.P0 Exp.P1 post.futility
single_stage 44 0.0895 0.1888 44 44 0.845
bayes_prob_single 44 0.1526 0.1161 44 44 0.91
bayes_prob_six 10,17,24,30,37,44 0.1364 0.268 24.62 26.46 0.91
bayes_like_six 11,17,24,30,37,44 0.1455 0.1606 29.53 32 0.91
post.efficacy post.toxicity post.no_toxicity
0.948 0.996 0.907
0.901 0.996 0.907
0.901 0.967 0.907
0.901 0.996 0.907
> ###############################################################
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.
