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, fitting non-linear dose-response models, a combination of testing and dose-response modelling and calculating optimal designs, both for normal and general response variable. In addition the package can be used to implement the MCP-Mod procedure, a combination of testing and dose-response modelling (Bretz et al. (2005), Pinheiro et al. (2014)).
You can install the development version of DoseFinding from GitHub with:
# install.packages("devtools")
devtools::install_github("openpharma/DoseFinding")library(DoseFinding)
data(IBScovars)
## set random seed to ensure reproducible adj. p-values for multiple contrast test
set.seed(12)
## 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
MCTtest(dose, resp, IBScovars, models=models,
addCovars = ~ gender)
#> Multiple Contrast Test
#>
#> Contrasts:
#> linear emax quadratic
#> 0 -0.616 -0.889 -0.815
#> 1 -0.338 0.135 -0.140
#> 2 0.002 0.226 0.294
#> 3 0.315 0.252 0.407
#> 4 0.638 0.276 0.254
#>
#> Contrast Correlation:
#> linear emax quadratic
#> linear 1.000 0.768 0.843
#> emax 0.768 1.000 0.948
#> quadratic 0.843 0.948 1.000
#>
#> Multiple Contrast Test:
#> t-Stat adj-p
#> emax 3.208 0.00128
#> quadratic 3.083 0.00228
#> linear 2.640 0.00848## 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)
optDesign(fmodels, weights, Delta=0.2, designCrit="TD")
#> Calculated TD - optimal design:
#> 0 10 25 50 100 150
#> 0.34960 0.09252 0.00366 0.26760 0.13342 0.15319This package was originally developed in 2010 and over the years has had
many different contributors. Some of the work on this package predates
its Github repository and we want to list here all contributors to the
package and highlight their contributions in addition to the “official””
package authors and maintainers as listed in the DESCRIPTION file.
- Marius Thomas — current maintainer
- Björn Bornkamp — former maintainer (until 2024)
- Björn Bornkamp
- Jose Pinheiro
- Frank Bretz
- Ludger Sandig — Vignettes
- Marius Thomas — Bayesian MCP-mod, various updates to code, docs, and tests
- Daniel Sabanes Bove — powMCTInterim implementation, longitudinal data vignette
- Carina Miller - Time-to-event vignette
Bretz, F., Pinheiro, J. C., and Branson, M. (2005), “Combining multiple comparisons and modeling techniques in dose-response studies,” Biometrics, Wiley Online Library, 61, 738–748. https://doi.org/10.1111/j.1541-0420.2005.00344.x.
Pinheiro, J., 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. https://doi.org/10.1002/sim.6052.