skeleton {dfped}

Build a working model.

Description

The construction of the working model's skeleton.

Usage

skeleton(doseChildren, doseAdult, dataTox, dataAuc = NULL, Clad,
         Clch, nbSimu, graph = TRUE)

Arguments

doseChildren

The paediatric dose level.

doseAdult

The adult dose level.

dataTox

The database of the toxicities.

dataAuc

The database of the AUC; defaults to NULL.

Clad

The clearance of the adults.

Clch

Paediatric clearance (known or estimated). An estimate can be computed using maturation adjustment (MA), allometric adjustment (AA) or linear adjustment (LA) for a specific group of age.

nbSimu

The number of simulation using in meta analysis function metaPhase.

graph

A choice to plot the estimates using the function plotEstimates in the end of the working model. Indicates graph = TRUE to plot or otherwise graph = FALSE; defaults to TRUE.

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>.

See Also

plotEstimates, metaPhase

Examples

 
## Not run: 
    ########
    # Note: For this example we are using a paediatric database that we have including data of 
    # children from 0 to 19 years old.
    ########
    
    children <- read.csv("/Users/artemistoumazi/paediatric_data_p3m/children_0_19.csv")
    AGE <- children$Age
    W <- children$Weight

    W_ad <- 70
    Cl_ad <- 3.95

    F_ad <- 0.6
    Eg <- 0
    Eh <- 0.058
    f_abs <- F_ad/((1 - Eh)*(1-Eg))
    fu_ad <- 1
    perc_CYPh <- data.frame("CYP3A4_5" = 0.7, "CYP1A2" = 0.3)
    perc_CYPg <- data.frame("CYP3A4_5" = 1)
    perc_alb <- 1
    perc_alpha1AG <- 0 

    data_molecule <- list(F_ad, f_abs, Eg, Eh, fu_ad, perc_CYPg, perc_CYPh, 
                          perc_alb, perc_alpha1AG)
    Clch_mat <- Clch.Mat(AGE, W, Cl_ad, W_ad, data_molecule)

    ####################################
    ########## WORKING MODEL ###########
    ####################################

    children <- data.frame(children, Clch_mat)

    ########## Children from 2 to 5 years old 
    children2_5 <- children[children$Age >= 2 & children$Age <= 5  ,]                          
    Cl_ch <-  mean(children2_5$Clch_mat)                    

    # Doses for paediatric using maturation adjustment
    dCh_mat_2_5 <- c(30, 45, 55, 70, 85)

    Cl_ad <- 3.95
    AUCThomas <- c(20,40, 60)
    probaToxThomas <- c(0.1,0.25, 0.55)


    ################# Non-parametric PAVA estimate ###################

    # data from the publications of toxicity in the erlotinib
    pardos_2006 <- rbind(c(100,0/3, 3), c(150, 1/3,3), c(200, 0/3, 3), c(250, 3/6, 6))
    thepot_2014 <- rbind(c(100, 0/5, 5), c(150,3/25, 25))
    calvo_2007 <- rbind(c(150, 1/25, 25))
    raizer_2010 <- rbind(c(150,11/99, 99))
    vanDenBent_2009 <- rbind( c(200, 6/54, 54))
    sheikh_2012 <- rbind(c(150, 0.544, 307)) 
    rocheNTC00531934 <- rbind(c(150, 0.186, 59)) 


    dataTox <- rbind(pardos_2006, thepot_2014, calvo_2007, raizer_2010, vanDenBent_2009, 
                     rocheNTC00531934, sheikh_2012)
    dataTox <- data.frame(dataTox)
    colnames(dataTox) <- c("doses", "proba", "nbPatients")
    nbTox <- dataTox$proba*dataTox$nbPatients
    dataTox <- data.frame(dataTox, nbTox)

    data_auc <- data.frame(AUCThomas, probaToxThomas )
    dose_children <- dCh_mat_2_5[1:4]
    dose_adult <- c(100,150,200, 250)
    graph <- TRUE

    skeleton(dose_children, dose_adult, dataTox, data_auc, Cl_ad, Cl_ch, nbSimu = 10, 
             graph = TRUE)

## End(Not run)