ss_npo {BayesOrdDesign}

Determine the sample size for Bayesian two-stage trial design of ordinal endpoints without proportional odds assumption

Description

Obtain estimated sample size based on user-specified type I error, power and effect size defined by the odds ratio between the treatment and control groups, without the proportional odds (PO) assumption.

Usage

ss_npo(nmax, or_alt, pro_ctr, U, alpha, power, ntrial, method)

Arguments

nmax

the maximum sample size for searching to get the desirable power

or_alt

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

alpha

the desirable type I error rate to be controlled

power

the desirable power to be achieved

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 50.

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.

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

Value

ss_npo() returns recommended sample size for each of two groups for the interim and final stages, by assuming 1:1 equal randomization for the two groups at each stage; and corresponding power.

Examples

set.seed(123)
ss_npo(nmax = 200, or_alt = c(1.6,1.5,1.5,1.4,1.4),
       pro_ctr = c(0.58,0.05,0.17,0.03,0.04,0.13), U = c(100,80,65,25,10,0),
       alpha = 0.05, power = 0.8, ntrial = 5, method = "Frequentist")