Using R package to implement Bayesian phase I/II dose-finding design for three outcomes

Ran20

Registered
#1
Dear all,

I am trying to implement the work done by Suyu Liu, “A Bayesian Phase I/II Trial Design for Immunotherapy”, using R, since the code attached with that work takes a lot of time (more than 20 hours, and the code not complete). So that I tried to use trialr package since it used rstan, but this package allowed me to use two outcomes ( toxicity, efficacy ) and the work of Liu used three outcomes (immune response, toxicity, and efficacy).

I tried to use the R package trialr for two outcomes using the utility for sensitivity analysis written in R code below (table 1 in the article), I want to see if I used the correct utility and to see how to add a third outcome ( immune response ) to the model. I attached to you the code and the output if you know how he produced the results, I hope you can feed me back.

and thanks in advance.


````
### My code###
rm(list = ls())
library(trialr)

#Utility
Uti <- array(0,c(2,3,2)) # order: tox, eff, immuno
Uti[,,1] <- matrix(c(0,0,50,10,80,35),nrow=2)
Uti[,,2] <- matrix(c(5,0,70,20,100,45),nrow=2)
N.max= 60 # patients
outcomes <- '1NNN 2NNT 3NNT 4NNN 5NTN'
doses = c(.1,.3,.5,.7,.9)


fit <- stan_efftox(outcomes,
real_doses =doses,
efficacy_hurdle = 0.5, toxicity_hurdle = 0.3,
p_e = 0.1, p_t = 0.1,
eff0 = 0.5, tox1 = 0.65,
eff_star = 0.7, tox_star = 0.25,
alpha_mean = -7.9593, alpha_sd = 3.5487,
beta_mean = 1.5482, beta_sd = 3.5018,
gamma_mean = 0.7367, gamma_sd = 2.5423,
zeta_mean = 3.4181, zeta_sd = 2.4406,
eta_mean = 0, eta_sd = 0.2,
psi_mean = 0, psi_sd = 1,
seed = 123)

ndoses <- length(fit$prob_tox)
plot(1:ndoses, fit$prob_tox, type="b", pch=19, xlab="Dose level", ylab="Probability of toxicity", ylim=c(0,max(fit$prob_tox) + 0.15), col="green")
points(1:ndoses,fit$prob_eff, type="b", pch=18, col="blue")
abline(h=0.3, lwd=2, lty=4, col = "red")
legend(1, 0.4, legend=c("Toxicity", "Effecacy"),
col=c("green", "blue"), lty=1:2, cex=0.8)
````
 

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Miner

Forum Moderator
Leader
Admin
#3
You may not find many people here that understand Bayesian statistics or R. I recommend that you also post this on TalkStats. There are several Bayesian experts as well as R experts there. However, they will know nothing about medical device standards.
 

Tidge

Trusted Information Resource
#4
I have to agree with @Miner . It is possible that someone here has enough familiarity with the precise tool, but the type of analysis you are trying to do isn't even in the same ballpark as medical device design/manufacturing so any specific feedback you would get from us here is likely to be 'uneven'.
 

Ran20

Registered
#5
I have to agree with @Miner . It is possible that someone here has enough familiarity with the precise tool, but the type of analysis you are trying to do isn't even in the same ballpark as medical device design/manufacturing so any specific feedback you would get from us here is likely to be 'uneven'.
I did, it is in the biostatics section since yesterday, and no response even yet



Thanks, @Miner, and @Tidge
 

Ran20

Registered
#7
I did, it is in the biostatics section since yesterday, and no response even yet



Thanks, @Miner, and @Tidge
My goal is: to use their idea to select the best dose in the first stage and to continue in a second stage with only 2 arms clinical trial and the best dose. (may there is another way to do that?) That why I am looking for the complete tool.
 
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