BayesOrdDesign (version 0.1.2)

get_oc_PO: Generate operating characteristics for Bayesian two-stage trial design of ordinal endpoints with proportional odds assumption

Description

Obtain operating characteristics (OC) of the Bayesian two-stage trial design of ordinal endpoints with proportional odds assumption.

Usage

get_oc_PO(alpha, pro_ctr, nmax, fixed_es, ormax, fixed_ss, ntrial, method)

Value

get_oc_PO() returns the operating characteristics of design as a table, including: (1) user-defined value, either sample size or effect size (2) corresponding power (3) average sample size

Arguments

alpha

the desirable type I error rate to be controlled

pro_ctr

distribution of clinical categories for the control group

nmax

the maximum sample size for operating characteristics

fixed_es

fixed effect size when simulate the OC for various sample size

ormax

the maximum effect size for OC

fixed_ss

fixed sample size when simulate the OC for various effect size

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

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.

Two types of operating characteristics can be implemented through this function.

Please note, in our example, argument ntrial = 5 is for the time saving purpose.

Examples

get_oc_PO(alpha = 0.05, pro_ctr = c(0.58,0.05,0.17,0.03,0.04,0.13),
          ormax = 1.5, fixed_ss = 150,
          ntrial = 5, method = "Frequentist")


get_oc_PO(alpha = 0.05, pro_ctr = c(0.58,0.05,0.17,0.03,0.04,0.13),
          nmax = 200, fixed_es = 1.5,
          ntrial = 5, method = "Frequentist")