MCPMod-package {MCPMod} | R Documentation |
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.
Package: | MCPMod |
Type: | Package |
Version: | 1.0-2 |
Date: | 2009-03-04 |
License: | GPL-3 |
Bjoern Bornkamp, Jose Pinheiro and Frank Bretz
Maintainer: Bjoern Bornkamp <bornkamp@statistik.tu-dortmund.de>
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
# detailed information regarding MCP-Mod methodology # and R-package available via vignette("MCPMod") # 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)