simulation: Simulate one or "n" dose-finding trials in paediatrics.

In artemis-toumazi/dfped: Extrapolation and Bridging of Adult Information in Early Phase Dose-Finding Paediatrics Studies

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 [e-mail], Caroline Petit [e-mail], Sarah Zohar [e-mail]

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

simu

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)

artemis-toumazi/dfped documentation built on May 10, 2019, 1:49 p.m.