fitPEM: Get the fitted DLT free survival (piecewise exponential...

In Roche/crmPack: Object-Oriented Implementation of CRM Designs

Get the fitted DLT free survival (piecewise exponential model). This function returns a data frame with dose, middle, lower and upper quantiles for the PEM curve. If hazard=TRUE,

Description

Get the fitted DLT free survival (piecewise exponential model). This function returns a data frame with dose, middle, lower and upper quantiles for the PEM curve. If hazard=TRUE,

Usage

fitPEM(
  object,
  model,
  data,
  quantiles = c(0.025, 0.975),
  middle = mean,
  hazard = FALSE,
  ...
)

## S4 method for signature 'Samples,DALogisticLogNormal,DataDA'
fitPEM(
  object,
  model,
  data,
  quantiles = c(0.025, 0.975),
  middle = mean,
  hazard = FALSE,
  ...
)

Arguments

object	mcmc samples
model	the mDA-CRM model
data	the data input, a DataDA class object
quantiles	the quantiles to be calculated (default: 0.025 and 0.975)
middle	the function for computing the middle point. Default: mean
hazard	should the the hazard over time be plotted based on the PEM? (not default) Otherwise ...
...	additional arguments for methods

Functions

• fitPEM(object = Samples, model = DALogisticLogNormal, data = DataDA): This method works for the DALogisticLogNormal model class.

Examples

# nolint start

# Create the data
data <- DataDA(
  x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
  y = c(0, 0, 1, 1, 0, 0, 1, 0),
  ID = 1L:8L,
  cohort = as.integer(c(1:5, 6, 6, 6)),
  doseGrid =
    c(
      0.1, 0.5, 1.5, 3, 6,
      seq(from = 10, to = 80, by = 2)
    ),
  u = c(42, 30, 15, 5, 20, 25, 30, 60),
  t0 = c(0, 15, 30, 40, 55, 70, 75, 85),
  Tmax = 60
)

# Initialize the CRM model used to model the data
npiece_ <- 10
lambda_prior <- function(k) {
  npiece_ / (data@Tmax * (npiece_ - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece_,
  l = as.numeric(t(apply(as.matrix(c(1:npiece_), 1, npiece_), 2, lambda_prior))),
  c_par = 2
)

# Obtain the posterior

options <- McmcOptions(
  burnin = 10,
  step = 2,
  samples = 1e2
)

set.seed(94)
samples <- mcmc(data, model, options)


# Extract the posterior mean hazard (and empirical 2.5 and 97.5 percentile)
# for the piecewise exponential model
# If hazard=FALSE, the posterior PEM will be plot
fitted <- fitPEM(
  object = samples,
  model = model,
  data = data,
  middle = mean,
  hazard = TRUE,
  quantiles = c(0.25, 0.75)
)

# nolint end

Roche/crmPack documentation built on April 30, 2024, 3:19 p.m.