MCPMod (version 1.0-10.1)

MCPMod-package: Design and Analysis of Dose-Finding Studies

Description

This package implements a methodology for dose-response analysis that combines aspects of multiple comparison procedures and modeling approaches (Bretz, Pinheiro and Branson, (2005)). The package provides tools for the analysis of dose finding trials as well as a variety of tools necessary to plan a trial to be conducted with the MCPMod methodology. **Note: The MCPMod package will not be further developed, all future development of the MCP-Mod methodology will be done in the DoseFinding R-package, which already contains an extended version of MCP-Mod, and additional functions useful for planning and analysing dose-finding trials.**

Details

Package:	MCPMod
Type:	Package
Version:	1.0-9
Date:	2016-11-24
License:	GPL-3

References

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

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

Pinheiro, J. C., Bretz, F., and Branson, M. (2006). Analysis of dose-response studies - modeling approaches, in N. Ting (ed.). Dose Finding in Drug Development, Springer, New York, pp. 146--171

Examples

# NOT RUN {
# detailed information regarding MCP-Mod methodology
# and R-package available via vignette("MCPMod")
# }
# NOT RUN {
# planning a trial for MCPMod
doses <- c(0,10,25,50,100,150)                                             
models <- list(linear = NULL, emax = c(25),                                
               logistic = c(50, 10.88111), exponential = c(85),            
               betaMod = matrix(c(0.33, 2.31, 1.39, 1.39), byrow=TRUE,nrow=2))
plotModels(models, doses, base = 0, maxEff = 0.4, scal = 200) 
sSize <- sampSize(models, doses, base = 0, maxEff = 0.4, sigma = 1,             
           upperN = 80, scal = 200, alpha = 0.05)
sSize
plM <- planMM(models, doses, n = rep(sSize$samp.size,6), scal=200, alpha = 0.05)
plM
plot(plM)

# analysing a trial
data(biom)
models <- list(linear = NULL, linlog = NULL, emax = 0.2,
            exponential = c(0.279,0.15), quadratic = c(-0.854,-1))

dfe <- MCPMod(biom, models, alpha = 0.05, dePar = 0.05, pVal = TRUE,
           selModel = "maxT", doseEst = "MED2", clinRel = 0.4, off = 1)
# detailed information is available via summary
summary(dfe)
# plots data with selected model function
plot(dfe, complData = TRUE, cR = TRUE)
# }