MCPModAnalysis: MCPMod-based analysis of dose-finding clinical trials with...

In MCPModPack: Simulation-Based Design and Analysis of Dose-Finding Trials

MCPMod-based analysis of dose-finding clinical trials with normally distributed, binary and count endpoints

Description

This function implements the MCPMod-based analysis of dose-finding clinical trials with normally distributed, binary and count endpoints, including derivation of the optimal contrasts for the candidate dose-response models, evaluation of dose-response tests based on the optimal contrasts, selection of the significant dose-response models and estimation of the target dose. For more information, see the technical manual in the package's doc folder.

Usage

MCPModAnalysis(endpoint_type, models, dose, resp, alpha, direction, 
               model_selection, Delta, theta)

Arguments

endpoint_type	Character value defining the primary endpoint's type. Possible values: "Normal": Normally distributed primary endpoint. "Binary": Binary primary endpoint. "Count": Count-type primary endpoint.
models	List of candidate dose-response models with initial values of the non-linear model parameters. The package supports the following dose-response models: linear, quadratic, exponential, Emax, logistic and sigEmax. No initial value is required for the linear model, a single initial value is required for the quadratic, exponential and Emax models, and two initial values are required for the logistic and sigEmax models.
dose, resp	Numeric vectors of equal length specifying the dose and response values.
alpha	Numeric value defining the one-sided significance level (default value is 0.025).
direction	Character value defining the direction of the dose-response relationship. Possible values: "Increasing": A larger value of the treatment difference corresponds to a beneficial treatment effect. "Decreasing": A smaller value of the treatment difference indicates a beneficial treatment effect.
model_selection	Character value defining the criterion for selecting the best dose-response model. Possible values: "AIC": Akaike information criterion (AIC). "maxT": Most significant test statistic. "aveAIC": Weighted AIC-based criterion.
Delta	Numeric value specifying the treatment effect for identifying the target dose. The treatment effect is defined relative to the placebo effect. A positive value is required if direction = "Increasing" and a negative value is required otherwise.
theta	Numeric vector defining the overdispersion parameter in each trial arm (required only with count-type primary endpoints).

Value

The function returns an object of class ‘⁠MCPModAnalysisResults⁠’. This object is a list with the following components:

input_parameters	a list containing the user-specified parameters, e.g, endpoint type, model selection criteria, etc.
selected_models	a logical vector defining the candidate dose-response models.
descriptive_statistics	a list containing the descriptive statistics computed from the trial's data set.
contrast_results	a list containing the contrast evaluation results for the candidate dose-response models, e.g., the model-specific optimal dose-response contrasts and contrast correlation matrix.
mcp_results	a list containing the multiplicity adjustment results for the candidate dose-response models, e.g., the model-specific test statistics and adjusted p-values.
mod_results	a list containing the modeling results for the candidate dose-response models, e.g., estimated model parameters, target dose estimate.

A detailed summary of the MCPMod analysis results can be generated using the AnalysisReport function.

Author(s)

Alex Dmitrienko <[e-mail]>

See Also

MCPModSimulation

Examples

  
# MCPMod-based analysis of a dose-finding trial with a binary endpoint

# Endpoint type
endpoint_type = "Binary"

# 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 = 0.3

# Perform an MCPMod-based analysis of the trial's data
# The data set binary is included in the package
results = MCPModAnalysis(endpoint_type = endpoint_type, 
                     models = models, 
                     dose = binary$dose, 
                     resp = binary$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 (Binary endpoint)", 
  tempfile("MCPMod analysis summary (Binary endpoint).docx", fileext=".docx")) 
  

MCPModPack documentation built on May 31, 2023, 5:23 p.m.