DoseFinding (version 0.5-5)

DoseFinding-package: Package overview

Description

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 (MCPtest), fitting non-linear dose-response models (fitDRModel), calculating optimal designs (calcOptDesign) and an implementation of the MCPMod methodology (MCPMod). For non-normal endpoints see gMCPtest and gFitDRModel.

Details

ll{ Package: DoseFinding Type: Package Version: 0.5-5 Date: 2011-08-31 License: GPL-3 LazyLoad: yes } The main functions are: MCPtest, implementing multiple contrast tests, fitDRModel that fits non-linear dose-response models, calcOptDesign that calculate optimal designs and MCPMod, which implements the MCPMod methodology. gMCPtest and gFitDRModel are generalizations of MCPtest and fitDRModel for non-normal data.

References

Bornkamp, B., Bretz, F., Dette, H. and Pinheiro, J. C. (2011). Response-Adaptive Dose-Finding under model uncertainty, to appear in Annals of Applied Statistics Bornkamp B., Pinheiro J. C., and Bretz, F. (2009). MCPMod: An R Package for the Design and Analysis of Dose-Finding Studies, Journal of Statistical Software, 29(7), 1--23 Bretz, F., Pinheiro, J. C., and Branson, M. (2005), Combining multiple comparisons and modeling techniques in dose-response studies, Biometrics, 61, 738--748

Dette, H., Bretz, F., Pepelyshev, A. and Pinheiro, J. C. (2008). Optimal Designs for Dose Finding Studies, Journal of the American Statisical Association, 103, 1225--1237 Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies combining multiple comparisons and modeling procedures, Journal of Biopharmaceutical Statistics, 16, 639--656 Seber, G.A.F. and Wild, C.J. (2003). Nonlinear Regression, Wiley

Examples

data(IBScovars)

# perform (model based) multiple contrast test
modlist <- list(linear = NULL, emax = 0.2, quadratic = -0.17)
# plot model shapes (need to specify base and maxEff additionally)
plotModels(modlist, c(0,4), base = 0, maxEff = 1)
fittest <- MCPtest(resp ~ dose, IBScovars, modlist, addCovars = ~ gender)

# fit non-linear dose-response model
fitemax <- fitDRModel(resp ~ dose, IBScovars, "emax")
# display fitted dose-effect curve
plot(fitemax)
# estimate minimum effective dose
MED(fitemax, clinRel = 0.25)

# MCP-Mod is a combination of (model based) multiple contrast tests
# and nonlinear regression
models <- list(linear = NULL, emax = 0.2, quadratic = -0.17)
dfe <- MCPMod(resp ~ dose, IBScovars, models, addCovars = ~gender,
              pVal = TRUE, selModel = "maxT",
              doseEst = "MED2", clinRel = 0.25)
# detailed information is available via summary
summary(dfe)
# plots data with selected model function
plot(dfe)

# Calculate optimal designs for MED estimation
doses <- c(0, 10, 25, 50, 100, 150)
mods <- list(linear = NULL, emax = 25, exponential = 85,
               linlog = NULL, logistic = c(50, 10.8811))
fMod <- fullMod(mods, doses, base=0, maxEff=0.4, off=1)
weights <- rep(1/5, 5)
desMED <- calcOptDesign(fMod, weights, doses, clinRel=0.2, scal=200,
                        off=1, method = "nlminb")