MCPModPack-package | R Documentation |
The MCPModPack package facilitates the design and analysis of dose-finding clinical trials with normally distributed, binary and count endpoints using the MCPMod methodology.
Package: | MCPModPack |
Type: | Package |
Version: | 0.3 |
Date: | 2020-08-01 |
License: | GPL-3 |
Key functions included in the package:
MCPModAnalysis
: Analyze data from a dose-finding trial using MCPMod.
AnalysisReport
: Generate a detailed summary of MCPMod analysis results in a Microsoft Word format.
AnalysisApp
: Launch a Shiny-based graphical user interface to analyze data from a dose-finding trial.
MCPModSimulation
: Perform a simulation-based evaluation of dose-finding trial designs using MCPMod.
SimulationReport
: Generate a detailed summary of MCPMod simulation results in a Microsoft Word format.
SimulationApp
: Launch a Shiny-based graphical user interface to perform a simulation-based evaluation of dose-finding trial designs.
The package comes with three example data sets:
normal
: Data set based on a dose-finding trial with a normally distributed endpoint.
binary
: Data set based on a dose-finding trial with a binary endpoint.
count
: Data set based on a dose-finding trial with a count endpoint.
Alex Dmitrienko <admitrienko@mediana.us>
Bornkamp, B., Bezlyak, V., Bretz, F. (2015). Implementing the MCP-Mod procedure for dose-response testing and estimation. Modern Approaches to Clinical Trials Using SAS. Menon, S., Zink, R. (editors). SAS Press: Cary, NC.
Bretz, F., Pinheiro, J.C., Branson, M. (2005). Combining multiple comparisons and modeling techniques in dose response studies. Biometrics. 61, 738-748.
Bretz, F., Tamhane, A.C., Pinheiro, J. (2009). Multiple testing in dose response problems. Multiple Testing Problems in Pharmaceutical Statistics. Dmitrienko, A., Tamhane, A.C., Bretz, F. (editors). New York: Chapman and Hall/CRC Press.
Nandakumar, S., Dmitrienko, A., Lipkovich, I. (2017). Dose-finding methods. Analysis of Clinical Trials Using SAS: A Practical Guide (Second Edition). Dmitrienko, A., Koch, G.G. (editors). SAS Press: Cary, NC.
Pinheiro, J. C., Bornkamp, B., 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., Bornkamp B., Glimm E., Bretz F. (2013). Model-based dose finding under model uncertainty using general parametric models. Statistics in Medicine. 33, 1646-1661.
# MCPMod-based analysis of a dose-finding trial with a normally distributed endpoint
# Select the candidate dose-response models and initial values
# of the non-linear model parameters (linear, quadratic, exponential,
# emax, logistic and sigemax)
models = list(linear = NA,
quadratic = -0.5,
exponential = 0.3,
emax = 0.3,
logistic = c(0.5, 0.1),
sigemax = c(0.5, 5))
# One-sided Type I error rate
alpha = 0.025
# Direction of the dose-response relationship
direction = "increasing"
# Model selection criterion
model_selection = "AIC"
# The treatment effect for identifying the target dose
# (this effect is defined relative to the placebo effect)
Delta = 0.5
# Perform an MCPMod-based analysis of the trial's data
# The data set normal is included in the package
results = MCPModAnalysis(endpoint_type = "Normal",
models = models,
dose = normal$dose,
resp = normal$resp,
alpha = alpha,
direction = direction,
model_selection = model_selection,
Delta = Delta)
# Simple summary of the MCPMod analysis results
results
# Detailed summary of the MCPMod analysis results (remove tempfile)
AnalysisReport(results,
"MCPMod analysis summary (Normally distributed endpoint)",
tempfile("MCPMod analysis summary (Normally distributed endpoint).docx", fileext=".docx"))
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