MCPModPack-package {MCPModPack}

Design and analysis of dose-finding trials

Description

The MCPModPack package facilitates the design and analysis of dose-finding clinical trials with normally distributed, binary and count endpoints using the MCPMod methodology.

Details

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.

Author(s)

Alex Dmitrienko <[e-mail]>

References

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.

Examples

    
# 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"))