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
sampSizeMCT for sample size
calculation), fitting non-linear dose-response models (
fitMod for ML
bFitMod for Bayesian and bootstrap/bagging ML
estimation), calculating optimal designs (
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.
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 models plot(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
fitemax <- fitMod(dose, resp, data=IBScovars, model="emax", bnds = c(0.01,5)) ## display fitted dose-effect curve plot(fitemax, CI=TRUE, plotData="meansCI")
## optimal design for estimation of the smallest dose that gives an ## improvement of 0.2 over placebo, a model-averaged design criterion ## is used (over the models defined in Mods) doses <- c(0, 10, 25, 50, 100, 150) fmodels <- Mods(linear = NULL, emax = 25, exponential = 85, logistic = c(50, 10.8811), doses = doses, placEff=0, maxEff=0.4) plot(fmodels, plotTD = TRUE, Delta = 0.2) weights <- rep(1/4, 4) desTD <- optDesign(fmodels, weights, Delta=0.2, designCrit="TD") desTD plot(desTD, fmodels)
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