ggplot(IBScovars, aes(x=dose, y=resp, color=gender)) +
geom_point(position = position_dodge(width = 0.5)) 
## set random seed to ensure reproducible adj. p-values for multiple contrast test
set.seed(12)
## perform (model based) multiple contrast test
## define candidate dose-response shapes
models <- Mods(linear = NULL, emax = 0.2, quadratic = -0.17,
doses = c(0, 1, 2, 3, 4))
## plot models
plot(models)
## fit non-linear emax dose-response model
fitemax <- fitMod(dose, resp, data=IBScovars, model="emax",
bnds = c(0.01,5))
## display fitted dose-effect curve
plot(fitemax, CI=TRUE, plotData="meansCI")
## fit non-linear emax dose-response model
fitlogistic <- fitMod(dose, resp, data=IBScovars, model="exponential")
## display fitted dose-effect curve
plot(fitlogistic, CI=TRUE, plotData="meansCI")
## perform multiple contrast test
MCTtest(dose, resp, IBScovars, models=models,
addCovars = ~ gender)
#> Multiple Contrast Test
#>
#> Contrasts:
#> linear emax quadratic
#> 0 -0.616 -0.889 -0.815
#> 1 -0.338 0.135 -0.140
#> 2 0.002 0.226 0.294
#> 3 0.315 0.252 0.407
#> 4 0.638 0.276 0.254
#>
#> Contrast Correlation:
#> linear emax quadratic
#> linear 1.000 0.768 0.843
#> emax 0.768 1.000 0.948
#> quadratic 0.843 0.948 1.000
#>
#> Multiple Contrast Test:
#> t-Stat adj-p
#> emax 3.208 0.00128
#> quadratic 3.083 0.00228
#> linear 2.640 0.00848library(DoseFinding)
library(ggplot2)
## example for creating a "full-model" candidate set placebo response
## and maxEff already fixed in Mods call
doses <- c(0, 10, 25, 50, 100, 150)
fmodels <- Mods(linear = NULL, emax = 25,
logistic = c(50, 10.88111), exponential = 85,
betaMod = rbind(c(0.33, 2.31), c(1.39, 1.39)),
linInt = rbind(c(0, 1, 1, 1, 1),
c(0, 0, 1, 1, 0.8)),
doses=doses, placEff = 0, maxEff = 0.4,
addArgs=list(scal=200))
## calculate doses giving an improvement of 0.3 over placebo
## Target doses 63
TD(fmodels, Delta=0.3)
#> linear emax logistic exponential betaMod1 betaMod2
#> 112.500000 45.000000 62.095220 130.265330 4.880978 56.762044
#> linInt1 linInt2
#> 21.250000 43.750000
## discrete version
TD(fmodels, Delta=0.3, TDtype = "discrete", doses=doses)
#> linear emax logistic exponential betaMod1 betaMod2
#> 150 50 100 150 10 100
#> linInt1 linInt2
#> 25 50
## doses giving 50% of the maximum effect
DoseFinding::ED(fmodels, p=0.5)
#> linear emax logistic exponential betaMod1 betaMod2
#> 75.000000 18.750000 50.215409 104.517639 1.255838 37.337384
#> linInt1 linInt2
#> 17.500000 37.500000
DoseFinding::ED(fmodels, p=0.5, EDtype = "discrete", doses=doses)
#> linear emax logistic exponential betaMod1 betaMod2
#> 100 25 100 150 10 50
#> linInt1 linInt2
#> 25 50
plot(fmodels, plotTD = TRUE, Delta = 0.3)
Hothorn, T., Bretz, F., and Westfall, P. (2008). Simultaneous Inference in General Parametric Models, Biometrical Journal, 50, 346–363
Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based dose finding under model uncertainty using general parametric models, Statistics in Medicine, 33, 1646–1661