The DoseFinding package provides functions for the design and analysis
of dose-finding experiments (for example pharmaceutical Phase II
clinical trials). It provides functions for: multiple contrast tests
(MCTtest for analysis and powMCT, sampSizeMCT for sample size
calculation), fitting non-linear dose-response models (fitMod for ML
estimation and bFitMod for Bayesian and bootstrap/bagging ML
estimation), calculating optimal designs (optDesign or calcCrit
for evaluation of given designs), both for normal and general response
variable. In addition the package can be used to implement the MCP-Mod
procedure, a combination of testing and dose-response modelling
(MCPMod) (@bretz2005, @pinheiro2014). A number of vignettes cover
practical aspects on how MCP-Mod can be implemented using the
DoseFinding package. For example a FAQ document for
MCP-Mod, analysis approaches for normal and
binary data, sample size and power
calculations as well as handling data from more
than one dosing regimen in certain scenarios.

Below a short overview of the main functions.

Perform multiple contrast test

library(DoseFinding)
data(IBScovars)
head(IBScovars)
## 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 modelsplot(models)
## perform multiple contrast test## functions powMCT and sampSizeMCT provide tools for sample size## calculation for multiple contrast tests
test <-MCTtest(dose, resp, IBScovars, models=models,
addCovars =~ gender)
test

Fit non-linear dose-response models here illustrated with Emax model