model_response {MCPModBC}

Model Responser

Description

It calculates the model response and parameters of interest for a given distribution.

Usage

model_response(doses, distr, model.true, models.candidate)

Arguments

doses

a numeric vector indicating the doses that will be considered in the clinical trial.

distr

a character value indicating the distribution of the response variable. Currently, the only option available is 'weibull'.

model.true

a character value indicating the functional form of the true dose-response curve. Options are "constant", "linear", "linlog", "quadratic", "exponential", "emax", "sigmaEmax", "betaMod", "logistic", "linInt".

models.candidate

an object of class Mods. See more details in Mods.

Value

a data frame with dimension length(doses) \times 3 with the following columns: (1) model response (2) model parameter and (3) doses

Author(s)

Diniz, M.A., Gallardo D.I., Magalhaes, T.M.

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)

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

# shape parameter
sigma.true <- 0.5

# 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  
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)

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

lambda.true <- response$lambda
parm <- list(lambda = lambda.true, sigma = sigma.true)