DoseFinding-package {DoseFinding} | R Documentation |
Design and Analysis of dose-finding studies
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
(MCTtest
), fitting non-linear dose-response models
(fitMod
), a combination of testing and dose-response modelling
(MCPMod
), and calculating optimal designs (optDesign
),
both for normal and general response variable.
Details
The main functions are:
MCTtest: Implements a multiple contrast tests
powMCT: Power calculations for multiple contrast tests
fitMod: Fits non-linear dose-response models
optDesign: Calculates optimal designs for dose-response models
MCPMod: Performs MCPMod methodology
sampSize: General function for sample size calculation
Author(s)
Bjoern Bornkamp, Jose Pinheiro, Frank Bretz
Maintainer: Bjoern Bornkamp <bbnkmp@gmail.com>
References
Bornkamp, B., Bretz, F., Dette, H. and Pinheiro, J. C. (2011). Response-Adaptive Dose-Finding under model uncertainty, Annals of Applied Statistics, 5, 1611–1631
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
O'Quigley, J., Iasonos, A. and Bornkamp, B. (2017) Handbook of methods for designing, monitoring, and analyzing dose-finding trials, CRC press, Part 3: Dose-Finding Studies in Phase II
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., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based dose finding under model uncertainty using general parametric models, Statistics in Medicine, 33, 1646–1661
Seber, G.A.F. and Wild, C.J. (2003). Nonlinear Regression, Wiley
Examples
data(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
test <- MCTtest(dose, resp, IBScovars, models=models,
addCovars = ~ gender)
## fit non-linear emax dose-response model
fitemax <- fitMod(dose, resp, data=IBScovars, model="emax",
bnds = c(0.01,5))
## display fitted dose-effect curve
plot(fitemax, CI=TRUE, plotData="meansCI")
## Calculate optimal designs for target dose (TD) estimation
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")