BayesianHybridDesign (version 0.1.0)

DPP.analysis: Analysis of a Study Using Bayesian Hybrid Design using Dynamic Power Prior Framework

Description

This function Perform Analysis for a Study Using Bayesian Hybrid Design using Dynamic Power Prior Framework.

Usage

DPP.analysis(
  Yt = 39,
  nt = 60,
  Yc = 13,
  nc = 30,
  Ych = 90,
  nch = 200,
  nche = 30,
  a0c = 0.001,
  b0c = 0.001,
  a0t = 0.001,
  b0t = 0.001,
  delta_threshold = 0.1,
  method = "Empirical Bayes",
  theta = 0.5,
  eta = 1
)

Value

An object of class list with values:

• w: Borrowing weight.

• phat_pt_larger_pc: Posterior probability P(ORR_trt > ORR_ctrl | data).

• apost_c_trial, bpost_c_trial: Parameters for the posterior Beta distribution of the concurrent control arm response rate.

• apost_c_hca, bpost_c_hca: Parameters for the posterior Beta distribution of the hybrid control arm response rate.

• apost_t, bpost_t: Parameters for the posterior Beta distribution of the experimental arm response rate.

• m.t: Posterior median response rate for the experimental arm.

• m.c: Posterior median response rate for the concurrent control arm.

• m.hca: Posterior median response rate for the hybrid control arm.

Arguments

Yt

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

nt

A scalar. sample size for experimental arm.

Yc

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

nc

A scalar. sample size for control arm.

Ych

A scalar. Number of responders in historical control.

nch

A scalar. Total number of subjects in historical control.

nche

A scalar. maximum amount of borrowing in terms of equivalent number of subjects.

a0c

A scalar. Hyperprior for control response rate beta(a0c, b0c). Default 0.001.

b0c

A scalar. Hyperprior for control response rate beta(a0c, b0c). Default 0.001.

a0t

A scalar. Hyperprior for experimental response rate beta(a0t, b0t). Default 0.001.

b0t

A scalar. Hyperprior for experimental response rate beta(a0t, b0t). Default 0.001.

delta_threshold

Borrow when abs(pc_hat (current study) - pch) <= delta_threshold#'. Default 0.1.

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

# \donttest{
o <- DPP.analysis(Yt=39, nt=60, Yc=13, nc=30, Ych=90, nch=200, nche = 30,
a0c= 0.001, b0c= 0.001, a0t= 0.001, b0t= 0.001,
delta_threshold = 0.1, method = "Empirical Bayes",
theta = 0.5, eta = 1)
print(o)
# }