| simulation {dfped} | R Documentation |
Simulate one or "n" dose-finding trials in paediatrics.
Description
It starts the process of simulations for a required number of simulated trials and return NULL. A dataframe is saved in the url named as "save_name" with the number of rows equals to the number of simulations lines and 26 columns containing the different estimates, the selected dose of each trial, etc.
Usage
simulation(stanModel, scenarioTox, scenarioEff, nbSubjects,
nbSimu, skeletonTox, skeletonEff, targetTox, targetEff,
cohortSize, startingDose, sd = NULL, mu,
adaptivePrior, saveName)
Arguments
stanModel |
A compiled STAN model. |
scenarioTox |
Toxicity scenario for simulations, with the probability of toxicity for each dose; must be a vector of length the number of doses. |
scenarioEff |
Efficacy scenario for simulations; must be a vector of length the number of doses. |
nbSubjects |
The maximum number of allocated patients; must be an integer. |
nbSimu |
The number of simulated trials; must be an integer. |
skeletonTox |
The skeleton of toxicity for the BMA bivariate CRM or the bivariate CRM; must be a dataframe with the number of rows corresponding to the number of doses and the number of columns corresponding to the number of working models for toxicity. |
skeletonEff |
The skeleton of efficacy for the BMA bivariate CRM or the bivariate CRM; must be a dataframe with the number of rows corresponding to the number of doses and the number of columns corresponding to the number of working models for efficacy. |
targetTox |
Target/threshold of toxicity; must be a double. |
targetEff |
Target/threshold of efficacy; must be a double. |
cohortSize |
The size of the cohorts for the 3+3 based algorithm before kickoff of the CRM; must be an integer. |
startingDose |
First dose to be assigned; must be an integer. |
sd |
The standard deviation; defaults to NULL. |
mu |
The mean value which using the model. |
adaptivePrior |
TRUE if you want to use as a prior an adaptive prior; FALSE otherwise. |
saveName |
The name of the RData that simulation will be stored; must be a string. |
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>.
Zohar, S., et al, (2011) An approach to meta-analysis of dose-finding studies, Statistics in Medicine.
See Also
Examples
## Not run:
library("rstan")
adaptivePrior <- TRUE
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
####### Number of simulation desired ###########
nbSimu <- 10
################# CRM model ################
############# Prior Normal #################
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));
}'
stan_model <- stan_model(model_code = stancode)
################## 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)
################# Simulation BMA - Normal prior ################
simulation(stan_model, scenario_tox, scenario_eff, nbSubjects,
nbSimu, skeleton_tox, skeleton_eff, targetTox, targetEff,
cohortSize, startingDose, sd, mu, TRUE, tempfile())
## End(Not run)