Package: DoseFinding 1.4-2

Marius Thomas

DoseFinding: Planning and Analyzing Dose Finding Experiments

The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).

Authors:Bjoern Bornkamp [aut], Jose Pinheiro [aut], Frank Bretz [aut], Ludger Sandig [aut], Marius Thomas [aut, cre], Daniel Sabanes Bove [aut], Carina Miller [ctb], Novartis Pharma AG [cph, fnd]

DoseFinding_1.4-2.tar.gz
DoseFinding_1.4-2.zip(r-4.7-x86_64)DoseFinding_1.4-2.zip(r-4.7-arm64)DoseFinding_1.4-2.zip(r-4.6-x86_64)DoseFinding_1.4-2.zip(r-4.6-arm64)
DoseFinding_1.4-2.tar.gz(r-4.7-arm64)DoseFinding_1.4-2.tar.gz(r-4.7-x86_64)DoseFinding_1.4-2.tar.gz(r-4.6-arm64)DoseFinding_1.4-2.tar.gz(r-4.6-x86_64)
DoseFinding_1.4-2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
DoseFinding/json (API)

# Install 'DoseFinding' in R:
install.packages('DoseFinding', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/openpharma/dosefinding/issues

Pkgdown/docs site:https://openpharma.github.io

Uses libs:
  • openblas– Optimized BLAS
Datasets:
  • biom - Biometrics Dose Response data
  • glycobrom - Glycopyrronium Bromide dose-response data
  • IBScovars - Irritable Bowel Syndrome Dose Response data with covariates
  • migraine - Migraine Dose Response data
  • neurodeg - Neurodegenerative disease simulated longitudinal dose-finding data set

On CRAN:

Conda:

openblas

7.81 score 9 packages 162 scripts 4.7k downloads 8 mentions 49 exports 21 dependencies

Last updated from:3a8a77fd12. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK214
linux-devel-x86_64OK210
source / vignettesOK337
linux-release-arm64OK216
linux-release-x86_64OK235
windows-devel-arm64OK356
windows-devel-x86_64OK224
windows-release-arm64OK294
windows-release-x86_64OK192
wasm-releaseOK133

Exports:betaModbetaModGradbFitModbMCTtestcalcCritcritValdefBndsDesignMCPModAppEDemaxemaxGradexponentialexponentialGradfitModgAICgetRespguesstlinearlinearGradlinIntlinIntGradlinloglinlogGradlogisticlogisticGradmaFitModMCPModMCTpvalMCTtestModsmvpostmixmvtnorm.controloptControptDesignplanModplotContrplotModspowMCTpowMCTInterimpowNquadraticquadraticGradrndDesignsampSizesampSizeMCTsigEmaxsigEmaxGradtargNTD

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglatticelifecyclemvtnormR6rbibutilsRColorBrewerRdpackrlangS7scalesvctrsviridisLitewithr

Continuous data MCP-Mod
Background and Data | Design stage | Analysis stage | Multiple comparisons | Dose-response estimation | How to adjust for covariates? | References
Rendered fromanalysis_normal.Rmdusingknitr::rmarkdown

Last update: 2026-10-16
Started: 2021-06-25

Multiple Regimen MCP-Mod
Background | Candidate models | Multiple contrast test | Dose-response modelling | References
Rendered frommult_regimen.Rmdusingknitr::rmarkdown

Last update: 2026-10-16
Started: 2021-06-25

Binary Data MCP-Mod
Background and data set | Candidate models | Analysis without covariates | Analysis with covariates | Avoiding problems with complete seperation and 0 responders | Considerations around optimal contrasts at design stage and analysis stage | Power and sample size considerations | References
Rendered frombinary_data.Rmdusingknitr::rmarkdown

Last update: 2026-10-16
Started: 2021-06-25

Longitudinal Data MCP-Mod
Data Simulation Code | Calculate Mean Response from Emax Model | Generate Normal Mean Vector and Covariance Matrix | Simulate Longitudinal Outcomes | Cut Interim Data | Longitudinal Data Analysis | Completers Analysis | Repeated Measures Analysis | Generalized MCP-Mod | Final Analysis | Futility Interim Analysis | Simulation study | References
Rendered fromlongitudinal_data.Rmdusingknitr::rmarkdown

Last update: 2026-10-16
Started: 2025-07-09

MCP-Mod FAQ
Preliminaries | For which types of study designs can I use MCP-Mod? | What is the difference between the original and generalized MCP-Mod, and what type of response can generalized MCP-Mod handle? | How many doses do we need to perform MCP-Mod? | How to determine the doses to be used for a trial using MCP-Mod? | How to set up the candidate set of models? | Can MCP-Mod be used in trials without placebo control? | Why are bounds used for the nonlinear parameters in the fitMod function? | Should model-selection or model-averaging be used for analysis? | Which model selection criterion should be used? | How to deal with intercurrent events and missing data? | Can MCP-Mod be used in trials with multiple treatment regimens? | What about dose-response estimates, when the MCP part was (or some of the model shapes were) not significant? | References
Rendered fromfaq.Rmdusingknitr::rmarkdown

Last update: 2026-10-16
Started: 2021-06-25

Overview DoseFinding package
Perform multiple contrast test | Fit non-linear dose-response models here illustrated with Emax model | Calculate optimal designs, here illustrated for target dose (TD) estimation | References
Rendered fromoverview.Rmdusingknitr::rmarkdown

Last update: 2026-10-16
Started: 2021-06-25

Sample size calculations for MCP-Mod
Power for multiple contrast test versus group sample size | Power versus treatment effect | Power under mis-specification | Sample size based on metrics other than power for the multiple contrast test | References
Rendered fromsample_size.Rmdusingknitr::rmarkdown

Last update: 2026-10-16
Started: 2021-06-25

Time-to-Event Data MCP-Mod
Example trial | Analysis with MCP-Mod | MCP step | Dose-response estimation | Power and sample size calculations at design stage | Calculating required sample size for a trial | References
Rendered fromtime_to_event_data.Rmdusingknitr::rmarkdown

Last update: 2026-10-16
Started: 2026-10-16

Readme and manuals

Help Manual

Help pageTopics
DoseFinding: Planning and Analyzing Dose Finding ExperimentsDoseFinding-package DoseFinding
Fit a dose-response model using Bayesian or bootstrap methods.bFitMod coef.bFitMod plot.bFitMod predict.bFitMod
Biometrics Dose Response databiom
Performs Bayesian multiple contrast testbMCTtest
Calculate critical value for multiple contrast testcritVal
Calculates default bounds for non-linear parameters in dose-response modelsdefBnds
Start externally hosted DesignMCPMod Shiny AppDesignMCPModApp
Built-in dose-response models in DoseFindingbetaMod betaModGrad drmodels emax emaxGrad exponential exponentialGrad linear linearGrad linInt linIntGrad linlog linlogGrad logistic logisticGrad quadratic quadraticGrad sigEmax sigEmaxGrad
Fit non-linear dose-response modelAIC.DRMod coef.DRMod fitMod gAIC gAIC.DRMod logLik.DRMod plot.DRMod predict.DRMod vcov.DRMod
Glycopyrronium Bromide dose-response dataglycobrom
Calculate guesstimates based on prior knowledgeguesst
Irritable Bowel Syndrome Dose Response data with covariatesIBScovars
Fit dose-response models via bootstrap model averaging (bagging)maFitMod plot.maFit predict.maFit print.maFit
MCPMod - Multiple Comparisons and ModelingMCPMod plot.MCPMod predict.MCPMod
Calculate multiplicity adjusted p-values for multiple contrast testMCTpval
Performs multiple contrast testMCTtest
Migraine Dose Response datamigraine
Define dose-response modelsgetResp Mods plot.Mods plotMods
Prior to posterior updating for a multivariate normal mixturemvpostmix
Control options for pmvt and qmvt functionsmvtnorm-control mvtnorm.control
Neurodegenerative disease simulated longitudinal dose-finding data setneurodeg
Calculate optimal contrastsoptContr plot.optContr plotContr
Function to calculate optimal designscalcCrit optDesign plot.DRdesign rndDesign
Evaluate performance metrics for fitting dose-response modelsplanMod plot.planMod summary.planMod
Calculate power for multiple contrast testpowMCT
Calculate Conditional or Predictive Power for Multiple Contrast TestpowMCTInterim
Sample size calculationsplot.targN powN sampSize sampSizeMCT targN
Calculate dose estimates for a fitted dose-response model (via 'fitMod()', 'bFitMod()') or 'maFitMod()') or a 'Mods()' objectED Target doses TD