DoseFinding: DoseFinding-package – R documentation

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DoseFinding-package

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

Package:	DoseFinding
Type:	Package
Version:	0.9-17
Date:	2019-11-15
License:	GPL-3

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

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")

DoseFinding

Planning and Analyzing Dose Finding Experiments

v0.9-17

GPL-3

Authors

Bjoern Bornkamp [aut, cre] (<https://orcid.org/0000-0002-6294-8185>), Jose Pinheiro [ctb], Frank Bretz [ctb]

Initial release

2019-11-15