| simu {dfped} | R Documentation |
A simulation of a single dose-finding trials in paediatrics.
Description
Simulate a single dose-finding clinical trial with the given scenarios of toxicity and efficacy.
Usage
simu(targetTox, targetEff, skeletonTox, skeletonEff, startingDose,
nbSubjects, crmModel, cohortSize, scenarioTox, scenarioEff,
nbDesign, mu, sd = NULL, lesb, sigmaLI, sigmaHI, adaptivePrior)
Arguments
targetTox |
Target/threshold of toxicity; must be a integer/double. |
targetEff |
Target/threshold of efficacy; must be a integer/double. |
skeletonTox |
Skeleton of toxicity for the BMA bivariate CRM, or the bivariate CRM. Must be a dataframe with the number of row corresponding to the number of doses and the number of columns corresponding to the number of working models for toxicity. |
skeletonEff |
Skeleton of efficacy for the BMA bivariate CRM, or the bivariate CRM. Must be a dataframe with the number of row corresponding to the number of doses and the number of columns corresponding to the number of working models for efficacy. |
startingDose |
First dose to be assigned; must be an integer. |
nbSubjects |
Maximum number of allocated patients; must be an integer. |
crmModel |
A model for STAN in C++. |
cohortSize |
The size of the cohorts for the 3+3 based algorithm before kickoff of the CRM; must be an integer. |
scenarioTox |
Toxicity scenario for the simulations with the probability of toxicity for each dose; must be a vector of length the number of doses. |
scenarioEff |
Efficacy scenario for the simulations; must be a vector of length the number of doses. |
nbDesign |
The number of different designs for the model selection using the Watanabe-Akaike information criteria (WAIC); must be an integer. |
mu |
The mean value which the model is using. |
sd |
The standard deviation. |
lesb |
A vector consisting of the variables b. |
sigmaLI |
The standard deviation when the model using non-informative prior. |
sigmaHI |
The standard deviation when the model using informative prior. |
adaptivePrior |
TRUE if you want to use as a prior an adaptive prior; FALSE otherwise. |
Author(s)
Artemis Toumazi artemis.toumazi@gmail.com, Caroline Petit caroline.petit@crc.jussieu.fr, Sarah Zohar sarah.zohar@inserm.fr
References
Petit, C., et al, (2016) Unified approach for extrapolation and bridging of adult information in early phase dose-finding paediatric studies, Statistical Methods in Medical Research, <doi:10.1177/0962280216671348>.
See Also
Examples
## Not run:
library(rstan)
adaptivePrior <- TRUE
####### Targets ###########
targetTox <- 0.25 # target of toxicity
targetEff <- 0.20 # target of efficacy
####### Skeleton ###########
skeleton_tox1 <- c(0.10, 0.21, 0.33, 0.55, 0.76)
skeleton_tox2 <- c(0.21, 0.33, 0.55, 0.76, 0.88)
skeleton_tox3 <- c(0.05, 0.10, 0.21, 0.33, 0.55)
skeleton_tox4 <- c(0.025, 0.05,0.1, 0.21, 0.33)
skeleton_tox5 <- c(0.0125, 0.025, 0.05,0.1, 0.21)
skeleton_eff <- c(0.04937516, 0.20496890, 0.43388003, 0.64409781, 0.79313693)
skeleton_tox <- data.frame(skeleton_tox1, skeleton_tox2, skeleton_tox3,
skeleton_tox4, skeleton_tox5)
skeleton_eff <- data.frame(skeleton_eff, skeleton_eff, skeleton_eff,
skeleton_eff, skeleton_eff)
############# Priors ############
priorModel <- list(rep(1/5,5), 0.001)
sd <- 0.65
mu <- -0.34
####### Trial settings #############
startingDose <- 1
nbSubjects <- 15
cohortSize <- 3
nbDesign <- length(skeleton_tox[1,])
nbDoses <- length(scenario_tox)
lesb <- calcul.bi(skeleton_tox[,1], mu, a = NULL, "power_log", targetTox)
sigmaLI <- sigmaLI(skeleton_tox[,1], mu, a = NULL, "power_log", targetTox)
sigmaHI <- sigmaHI(skeleton_tox[,1], mu, a = NULL, "power_log", targetTox, 0.80)
################## Scenarios ##############
scenario_tox <- c(0.1301477, 0.2774171, 0.4184642, 0.6486846, 0.8257219)
scenario_eff <- c(0.07945205, 0.20000000, 0.33686856, 0.59537737, 0.80996173)
stancode <- 'data {
int <lower = 0> J; //nb of patients
int <lower = 0> K; // nb of doses and dose reference
real r[K]; // skeleton for tox - K doses
real q[K]; // skeleton for efficacy - K doses
int y[J]; // toxicity of patient j
int v[J]; // efficacy of patient j
int d[J]; // dose received by patient j
real moy; // mean for the normal prior of toxicity
real standardError; //standard error of the normal prior of toxicity
}
parameters {
real <lower = 0> alpha;
real <lower = 0> beta;
}
transformed parameters{
real <lower = 0, upper = 1> varphi[K]; // marginal probability of toxicity for dose k
real <lower = 0, upper = 1> psi[K]; // marginal probability of efficacy for dose k
// defining the marginal probabilities for each value of a and b for each dose
real p01[K]; // tox = 0, eff = 1
real p10[K]; // tox = 1, eff = 0
real p11[K]; // tox = 1, eff = 1
real p00[K]; // tox = 0, eff = 0
vector[J] logLike;
for (k in 1:K){
varphi[k] = exp(alpha*log(r[k]));
psi[k] = exp(beta*log(q[k]));
}
// computing the marginal probabilities for each dose
for (k in 1:K){
p01[k] = (1-varphi[k])*psi[k];
p10[k] = varphi[k]*(1-psi[k]);
p00[k] = (1-varphi[k])*(1-psi[k]);
p11[k] = varphi[k]*psi[k];
}
// Computing the log-likelihood
for (j in 1:J){
logLike[j] = y[j]*v[j]*log(p11[d[j]]) + y[j]*(1-v[j])*log(p10[d[j]])
+ (1-y[j])*v[j]*log(p01[d[j]]) + (1-y[j])*(1-v[j])*log(p00[d[j]]);
}
}
model {
// priors
alpha ~lognormal(moy, standardError);
beta ~ lognormal(0,sqrt(1.34));
increment_log_prob(sum(logLike));
}'
crm_model <- stan_model(model_code = stancode)
################## Simulation ##############
simu(targetTox, targetEff, skeleton_tox, skeleton_eff,
startingDose, nbSubjects, crm_model, cohortSize, scenario_tox,
scenario_eff, nbDesign, mu, sd = sd, lesb,
sigmaLI, sigmaHI, adaptivePrior)
## End(Not run)