summary.mcpmod_simulation {MCPModBC}

Summary of simulation results

Description

It summarizes results of simulations of dose-finding trials following the MCP-Mod approach with bias-corrected and second-order covariance matrices.

Usage

## S3 method for class 'mcpmod_simulation'
summary(object, ...)

Arguments

object

an object of the "mcpmod_simulation" class.

...

additional arguments affecting the summary produced.

Value

A data frame with a summary with the information provided by mcpmod_simulation.

References

Diniz, Márcio A. and Gallardo, Diego I. and Magalhães, Tiago M. (2023). Improved inference for MCP-Mod approach for time-to-event endpoints with small sample sizes. arXiv <doi.org/10.48550/arXiv.2301.00325>

Examples

library(DoseFinding)
library(MCPModBC)

## doses scenarios 
doses <- c(0, 5, 25, 50, 100)
nd <- length(doses)
sample.size <- 25

# shape parameter
sigma.true <- 0.5

# median survival time for placebo dose
mst.control <- 4 

# maximum hazard ratio between active dose and placebo dose 
hr.ratio <- 4  
# minimum hazard ratio between active dose and placebo dose
hr.Delta <- 2 

# hazard rate for placebo dose
placEff <- log(mst.control/(log(2)^sigma.true)) 

# maximum hazard rate for active dose
maxEff <- log((mst.control*(hr.ratio^sigma.true))/(log(2)^sigma.true))

# minimum hazard rate for active dose
minEff.Delta <- log((mst.control*(hr.Delta^sigma.true))/(log(2)^sigma.true))
Delta <- (minEff.Delta - placEff)
	
## MCP Parameters 
significance.level <- 0.05
selModel <- "AIC"

emax <- guesst(d = doses[4], p = 0.5, model="emax")
exp <- guesst(d = doses[4], p = 0.1, model="exponential", Maxd = doses[nd])
logit <- guesst(d = c(doses[3], doses[4]), p = c(0.1,0.8), "logistic",  
	Maxd= doses[nd])
betam <- guesst(d = doses[2], p = 0.3, "betaMod", scal=120, dMax=50, 
	Maxd= doses[nd])

models.candidate <- Mods(emax = emax, linear = NULL,
                         exponential = exp, logistic = logit,
                         betaMod = betam, doses = doses,
                         placEff = placEff, maxEff = (maxEff- placEff))
plot(models.candidate)

## Simulation Parameters
n.sim <- 10
seed <- 1234 
n.cores <- 1

## True Model
model.true <- "emax"
response <- model_response(doses = doses,
                           distr = "weibull", 
                           model.true = model.true, 
                           models.candidate = models.candidate) 
lambda.true <- response$lambda
parm <- list(lambda = lambda.true, sigma = sigma.true)

## Scenario: Censoring 10%
censoring.rate <- 0.1

test <- mcpmod_simulation(doses = doses,
           parm = parm,
           sample.size = sample.size,
           model.true = model.true,
           models.candidate = models.candidate,
           selModel = selModel,
           significance.level = significance.level,
           Delta = Delta,
           distr = "weibull",
           censoring.rate = censoring.rate,
           sigma.estimator = "jackknife",
           n.cores = n.cores, seed = seed, n.sim = n.sim)
summary(test)