BayesianHybridDesign (version 0.1.0)

EESS: Bayesian Hybrid Design

Description

This function calculates the expected effective sample size of the DPP.

Usage

EESS(
  pc,
  nc,
  pch,
  nche,
  nch,
  a0c = 0.001,
  b0c = 0.001,
  delta_threshold = 0.1,
  method = "Empirical Bayes",
  theta = 0.5,
  eta = 1
)

Value

The expected effective sample size.

Arguments

pc

A scalar. Response rate for control arm in current study.

nc

A scalar. Number of patients in control arm in current study.

pch

A scalar. Response rate for control treatment in historical study.

nche

A scalar. Equivalent number of patients borrowed from historical study.

nch

A scalar. Total number of patients in historical control.

a0c

A scalar. Hyperprior for control response rate beta(a0c, b0c).

b0c

A scalar. Hyperprior for control response rate beta(a0c, b0c).

delta_threshold

A scalar. Borrow when abs(pc_hat (current study) - pch) <= delta_threshold.

method

A string characters. Method for dynamic borrowing, "Empirical Bayes", "Bayesian p", "Generalized BC", "JSD". Default "Empirical Bayes".

theta

A scalar parameter with a range of (0, 1), and applicable to method: "Generalized BC". Default 0.5.

eta

A scalar parameter with a range of (0, infty), and applicable to method: "Bayesian p", "Generalized BC", "JSD". "Generalized BC" method requires two parameters theta and eta. Default 1.

Examples

EESS(pc=0.3,nc=40,pch=0.3,nche=40,nch=180, a0c=0.001,b0c=0.001,
delta_threshold=0.1,method="Empirical Bayes", theta=0.5, eta=1)