bayes_binom_two_postprob: Bayesian, single arm, two endpoint trial design, using...
In EurosarcBayes: Bayesian Single Arm Sample Size Calculation Software

Description Usage Arguments Details Value See Also Examples

View source: R/bayes_binom_two_postprob_v2.2.R

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.

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)

`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

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.

Returns an object of class trialDesign_binom_two

bayes_binom_two_postprob, bayes_binom_two_postlike,bayes_binom_two_loss

# 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)

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
cut-points at each analysis
  patient review low toxicity high toxicity poor outcome good outcome
1             10            0             3            0           NA
2             15            1             4            2           NA
3             20            2             5            3            7
4             25            3             5            4            9
5             30            4             6            6           10
6             35            5             7            7           11
7             40            7             8           11           12

Frequentist properties of design
                                  Stopping rules T=0.1, R=0.35 T=0.1, R=0.2
1                 Stop early - Futility/Toxicity         24.13        74.77
4 Continue to final analysis - Futility/Toxicity          6.86        13.98
2                          Stop early - Efficacy         64.79         9.94
3          Continue to final analysis - Efficacy          4.23         1.30
6          Expected number of patients recruited         23.91        22.29
  T=0.3, R=0.2 T=0.3, R=0.35
1        98.65         95.27
4         0.68          0.46
2         0.59          3.94
3         0.08          0.32
6        13.17         14.13

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 0.020 0.314 0.570 0.981 0.086  0.009    NA     NA
 15 0.026 0.515 0.450 0.983 0.352  0.045    NA     NA
 20 0.027 0.648 0.363 0.986 0.370  0.033 0.957  0.536
 25 0.026 0.741 0.163 0.960 0.383  0.024 0.977  0.573
 30 0.024 0.807 0.135 0.969 0.571  0.046 0.967  0.455
 35 0.022 0.855 0.112 0.976 0.566  0.033 0.958  0.356
 40 0.046 0.952 0.094 0.982 0.898  0.176 0.948  0.276

Futility     P(R<0.35)=0.824
Efficacy     P(R>0.2)=0.948

Toxicity ok  P(T<0.3)=0.954
Toxicity     P(T>0.1)=0.96                    n  alpha   beta Exp.P0 Exp.P1 post.futility post.efficacy
 10,15,20,25,30,35,40 0.1125 0.3098  22.29  23.91         0.824         0.948
 post.toxicity post.no_toxicity
          0.96            0.954