ss_switch: Determine the sample size for Bayesian two-stage trial design...

In BayesOrdDesign: Bayesian Group Sequential Design for Ordinal Data

Determine the sample size for Bayesian two-stage trial design for ordinal endpoints based on switch model

Description

When there lacks of sufficient information to determine which of these two models (PO or NPO) is more appropriate, PO/NPO switch model-based design is utilized to obtain estimated sample size based on user specified type I error, power and expected effect.

Usage

ss_switch(
  alpha,
  power,
  n_po,
  n_npo,
  or_alt,
  pro_ctr,
  U,
  ntrial,
  method,
  n_range
)

Arguments

alpha	the desirable type I error rate to be controlled
power	the desirable power to be achieved
n_po	sample size for the treatment and control groups, at each stage based on PO model
n_npo	sample size for the treatment and control groups, at each stage based on NPO model
or_alt	expected treatment efficacy effect size to be detected (under H_1) in terms of odds ratio
pro_ctr	distribution of clinical categories for the control group
U	the desirability of each outcome level
ntrial	the number of simulated trials
method	whether the statistical test for interim/final analysis is Bayesian or Frequentist. method = "Frequentist" for Frequentist approach; method = "Bayesian" for Bayesian approach
n_range	the additional sample size for each arm each stage after n_po, n_npo.

Details

Grid search of sample size is used for guarantee a desirable type I error rate. The upper limitation is 200, and lower limitation default is sample size 50 for the control and treatment groups at each stage. Default increment of the sequence is 10.

For the parameter estimation section, we have two options, and can be selected using the method argument.Two following options are available: (i) method = "Frequentist", (ii) method = "Bayesian". If method = "Frequentist", parameters are estimated via package ordinal, which is based on frequentist method, while method = "Bayesian", parameters are estimated through Bayesian model.

Specifically, the numerical utilities U reflect the desirability of each outcome level. To do this, in our example, we first set U[1] = 100 and U[5] = 0, and then asked physicians to specify numerical values for the intermediate levels, that reflect their desirability relative to the best and worst levels.

Arguments n_po and n_npo are the estimated sample size for the treatment and control groups at each stage based on PO model and NPO model respectively. Users can obtained them through function ss_po and ss_npo.

Value

ss_switch() returns recommended sample size for each group at every interim look, with assumption that the sample size in the control arm of the study is same as in the treatment arm, and the sample size at each interim look is same.

Examples

ss_switch(alpha = 0.05, power=0.8, n_po = 475, n_npo = 75, n_range = 10,
          or_alt = c(1.5,1.5,1.5,1.5,1.5), pro_ctr = c(0.58,0.05,0.17,0.03,0.04,0.13),
          U = c(100,80,65,25,10,0), ntrial = 5, method = "Frequentist")
         

BayesOrdDesign documentation built on Nov. 14, 2022, 5:07 p.m.