 #######################################
> ######## contB
> ########
> library(tidyverse)
-- Attaching core tidyverse packages ----------------------------------------------------- tidyverse 2.0.0 --
? dplyr     1.1.2     ? readr     2.1.4
? forcats   1.0.0     ? stringr   1.5.0
? ggplot2   3.4.2     ? tibble    3.2.1
? lubridate 1.9.2     ? tidyr     1.3.0
? purrr     1.0.1     
-- Conflicts ----------------------------------------------------------------------- tidyverse_conflicts() --
? dplyr::filter() masks stats::filter()
? dplyr::lag()    masks stats::lag()
? Use the conflicted package to force all conflicts to become errors
Warning message:
package ‘ggplot2’ was built under R version 4.4.0 
> library(clinDR)
Loading required package: rstan
Loading required package: StanHeaders

rstan version 2.26.22 (Stan version 2.26.1)

For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores()).
To avoid recompilation of unchanged Stan programs, we recommend calling
rstan_options(auto_write = TRUE)
For within-chain threading using `reduce_sum()` or `map_rect()` Stan functions,
change `threads_per_chain` option:
rstan_options(threads_per_chain = 1)

Do not specify '-march=native' in 'LOCAL_CPPFLAGS' or a Makevars file

Attaching package: ‘rstan’

The following object is masked from ‘package:tidyr’:

    extract

Loading required package: shiny
> 
> 
> # Design aspects
> nsim <- 50
> doselev <- c(0,5,25,50,150)
> n <- c(100, 50, 50, 50, 100)
> Ndose <- length(doselev)
> 
> # Parameters
> led50 <- log(15)
> lambda <- 1
> e0 <- 10
> target <- 6
> targetDose <- 175
> emax <- solveEmax(target = target, 
+                   dose = targetDose,
+                   led50 = led50,
+                   lambda = lambda,
+                   e0 = e0,
+                   pboadj = TRUE)
> 
> pop <- c(led50 = led50, lambda = lambda, emax = emax, e0 = e0)
> resSD <- 8
> 
> meanlev <- emaxfun(doselev, pop)
> gen <- FixedMean(n, doselev, meanlev, resSD, parm = pop)
> 
> # Priors
> prior <- emaxPrior.control(epmu = 5,
+                            epsca = 80,
+                            difTargetmu = 0,
+                            difTargetsca = 80,
+                            dTarget = 175,
+                            p50 = 15,
+                            sigmalow = 0.8,
+                            sigmaup = 70,
+                            parmDF = 5)
> # Stan and MCMC settings
> rstan::rstan_options(auto_write = TRUE)
> mcmc<-mcmc.control(chains=1,warmup=500,iter=5000,
+                    propInit=0.15,adapt_delta = 0.95)
> 
> # Simulate outcomes
> D1 <- emaxsimB(nsim, gen, prior,
+                modType=4,mcmc=mcmc,
+                check=FALSE, nproc=10,
+                binary = FALSE, seed=12357)
> summary(D1)

  

Number of simulations: 50

Power for 1-sided tests at level 0.05 :
  Global null test based on MCP-mod:                           1
  Pairwise comparison (simple, no model) of high dose vs pbo:  1


Coverage probabilities for nominal 0.9 intervals [Dose-PBO]:
Bayesian Dose response modeling posterior intervals:
   5   25   50  150 
0.98 0.94 0.92 0.92 

Pairwise comparisons:
   5   25   50  150 
0.88 0.88 0.92 0.90 
Most favorable pairwise comparison:
0.86  Intervals too low( 0.04 ) Intervals too high( 0.1)


Bias from Bayesian dose response modeling [DOSE-PBO, EST-POP, EST=posterior median]:
    5    25    50   150 
 0.13 -0.14 -0.18  0.01 
Bias in the most favorable pairwise comparison:
0.63 


Square Root Mean Squared Error [Dose-PBO]:
Bayesian dose response modeling (EST=posterior median) : 
    5    25    50   150 
0.720 0.888 0.907 0.969 

 Pairwise comparisons: 
    5    25    50   150 
1.501 1.382 1.295 1.066 

Most favorable pairwise comparison: 
1.271 

The mean of the proportion of diverent MCMC iterations:  0 
> plot(D1)
Population-Z is (posterior mean - pop)/(posterior sd)
> 
> # Additional plots of simulation results
> pairv<-select(mutate(as_tibble(D1$est),ED50 = exp(led50)),-led50)
> pairv<-rename(pairv,E0 = e0, 
+               Emax = emax) 
> pairv<-mutate(pairv,'Residual SD' = D1$residSD)
> pairs(pairv)
> 
> #######################################
> ######## cont
> ########
> library(tidyverse)
> library(clinDR)
> 
> 
> # Design aspects
> nsim <- 30
> doselev <- c(0,5,25,50,150)
> n <- c(100, 50, 50, 50, 150)
> Ndose <- length(doselev)
> 
> # mean values
> meanlev <- c(2.0, 3.72, 5.31, 7.31, 8.0)
> resSD <- 6
> 
> gen <- FixedMean(n, doselev, meanlev, resSD)
> 
> 
> 
> # Simulate outcomes
> D1 <- emaxsim(nsim, gen, modType=4,
+               nproc=5, binary = FALSE,
+               seed=12357)
> 
> summary(D1, testalph=0.05)

  

Number of simulations:                     30
Proportion failing to converge:            0.567
Proportion converged but ED50>upper limit: 0
Proportion converged but ED50<lower limit: 0
Proportions with modType:
   3    4 
0.57 0.43 

Power for 1-sided tests at level 0.05 :
  Global null test based on MCP-mod:                           1
  Pairwise comparison (simple, no model) of high dose vs pbo:  1


Coverage probabilities for nominal 0.9 intervals [Dose-PBO]:
Dose response modeling:
    5    25    50   150 
0.800 0.767 0.900 0.967 
Pairwise comparisons:
    5    25    50   150 
0.933 0.867 0.967 0.933 
Most favorable pairwise comparison:
0.967  Intervals too low( 0 ) Intervals too low( 0.033)


Bias from dose response modeling [DOSE-PBO, EST-POP]:
    5    25    50   150 
-0.16  0.40 -0.49 -0.09 
Bias in the most favorable pairwise comparison:
0.36 

Square Root Mean Squared Error:
Dose response modeling: 
    5    25    50   150 
0.864 1.083 0.814 0.632 

 Pairwise comparisons: 
    5    25    50   150 
1.013 1.075 0.880 0.721 

Most favorable pairwise comparison: 
0.837> plot(D1)
Results based on alternative model fits are displayed in red
> 
> # Additional plots of simulation results
> pairv<-select(mutate(as_tibble(D1$est4),ED50 = exp(led50)),-led50)
> pairv<-rename(pairv,E0 = e0, 
+               Emax = emax) 
> pairv<-mutate(pairv,'Residual SD' = D1$residSD)
> pairs(pairv)
> 
> 
> #######################################
> ######## binB
> ########
> library(tidyverse)
> library(clinDR)
> 
> 
> # Design aspects
> nsim <- 50
> doselev <- c(0,5,25,50,150,300)
> n <- c(100, 50, 50, 50, 150,50)
> Ndose <- length(doselev)
> 
> # Parameters
> led50 <- log(15)
> lambda <- 1
> e0 <- -1
> target <- 1.5
> targetDose <- 200
> emax <- solveEmax(target = target, 
+                   dose = targetDose,
+                   led50 = led50,
+                   lambda = lambda,
+                   e0 = e0,
+                   pboadj = TRUE)
> 
> pop <- c(led50 = led50, lambda = lambda, emax = emax, e0 = e0)
> meanlev <- plogis(emaxfun(doselev, pop))
> gen <- FixedMean(n, doselev, meanlev, parm = pop, binary = TRUE)
> 
> # Priors
> prior <- emaxPrior.control(epmu = 0,
+                            epsca = 4,
+                            difTargetmu = 0,
+                            difTargetsca = 4,
+                            dTarget = 200,
+                            p50 = 25,
+                            parmDF = 5,
+                            binary = TRUE)
> # Stan and MCMC settings
> rstan::rstan_options(auto_write = TRUE)
> mcmc<-mcmc.control(chains=1,warmup=500,iter=5000,
+                    propInit=0.15,adapt_delta = 0.95)
> 
> # Simulate outcomes
> D1 <- emaxsimB(nsim, gen, prior,
+                modType=4,mcmc=mcmc,
+                check=FALSE, nproc=10,
+                binary = TRUE, seed=12357)
> summary(D1)

  

Number of simulations: 50

Power for 1-sided tests at level 0.05 :
  Global null test based on MCP-mod:                           1
  Pairwise comparison (simple, no model) of high dose vs pbo:  1


Coverage probabilities for nominal 0.9 intervals [Dose-PBO]:
Bayesian Dose response modeling posterior intervals:
   5   25   50  150  300 
1.00 0.98 0.90 0.86 0.84 

Pairwise comparisons:
   5   25   50  150  300 
0.94 1.00 0.86 0.90 0.88 
Most favorable pairwise comparison:
0.84  Intervals too low( 0 ) Intervals too high( 0.16)


Bias from Bayesian dose response modeling [DOSE-PBO, EST-POP, EST=posterior median]:
    5    25    50   150   300 
 0.00 -0.01 -0.01  0.00  0.01 
Bias in the most favorable pairwise comparison:
0.06 


Square Root Mean Squared Error [Dose-PBO]:
Bayesian dose response modeling (EST=posterior median) : 
    5    25    50   150   300 
0.032 0.050 0.052 0.054 0.058 

 Pairwise comparisons: 
    5    25    50   150   300 
0.065 0.071 0.093 0.057 0.081 

Most favorable pairwise comparison: 
0.091 

The mean of the proportion of diverent MCMC iterations:  0 
> plot(D1)
Population-Z is (posterior mean - pop)/(posterior sd)
> 
> # Additional plots of simulation results
> pairv<-select(mutate(as_tibble(D1$est),ED50 = exp(led50)),-led50)
> pairs(pairv)
> 
> 
> #######################################
> ######## bin
> ########
> library(tidyverse)
> library(clinDR)
> 
> 
> # Design aspects
> nsim <- 50
> doselev <- c(0,5,25,50,150,300)
> n <- c(100, 50, 50, 50, 150,50)
> Ndose <- length(doselev)
> 
> # Parameters
> led50 <- log(15)
> lambda <- 1.5
> e0 <- -1
> target <- 1.5
> targetDose <- 200
> emax <- solveEmax(target = target, 
+                   dose = targetDose,
+                   led50 = led50,
+                   lambda = lambda,
+                   e0 = e0,
+                   pboadj = TRUE)
> 
> pop <- c(led50 = led50, lambda = lambda, emax = emax, e0 = e0)
> meanlev <- plogis(emaxfun(doselev, pop))
> gen <- FixedMean(n, doselev, meanlev, parm = pop, binary = TRUE)
> 
> 
> 
> # Simulate outcomes
> D1 <- emaxsim(nsim, gen, modType=4,
+               nproc=10, binary = TRUE,
+               seed=12357)
> 
> summary(D1, testalph=0.05)

  

Number of simulations:                     50
Proportion failing to converge:            0.24
Proportion converged but ED50>upper limit: 0
Proportion converged but ED50<lower limit: 0
Proportions with modType:
   3    4 
0.24 0.76 

Power for 1-sided tests at level 0.05 :
  Global null test based on MCP-mod:                           1
  Pairwise comparison (simple, no model) of high dose vs pbo:  1


Coverage probabilities for nominal 0.9 intervals [Dose-PBO]:
Dose response modeling:
   5   25   50  150  300 
0.84 0.92 0.90 0.84 0.88 
Pairwise comparisons:
   5   25   50  150  300 
0.96 0.94 0.90 0.92 0.88 
Most favorable pairwise comparison:
0.84  Intervals too low( 0 ) Intervals too low( 0.16)


Bias from dose response modeling [DOSE-PBO, EST-POP]:
   5   25   50  150  300 
0.02 0.01 0.00 0.01 0.02 
Bias in the most favorable pairwise comparison:
0.06 

Square Root Mean Squared Error:
Dose response modeling: 
    5    25    50   150   300 
0.051 0.061 0.053 0.054 0.059 

 Pairwise comparisons: 
    5    25    50   150   300 
0.066 0.073 0.091 0.057 0.080 

Most favorable pairwise comparison: 
0.091> plot(D1)
Results based on alternative model fits are displayed in red
> 
> # Additional plots of simulation results
> pairv<-select(mutate(as_tibble(D1$est4),ED50 = exp(led50)),-led50)
> pairs(pairv)
