BayesOrdDesign (version 0.1.2)

get_oc_NPO: Generate operating characteristics for Bayesian two-stage trial design of ordinal endpoints without proportional odds assumption

Description

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

Usage

get_oc_NPO(alpha, pro_ctr, U, fixed_ss, ors, nmax, fixed_es, ntrial, method)

Value

get_oc_NPO() 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 desired type I error to be controlled

pro_ctr

distribution of clinical categories for the control group

U

the desirability of each outcome level

fixed_ss

fixed sample size when simulates the OC for various effect size

ors

a user-defined matrix, each row denotes the various scenarios, the number of columns depend on the number of outcome scales.

nmax

the maximum sample size when simulates the OC for different sample size, the increment is 50 and the initial sample size is 50 for each arm each stage.

fixed_es

fixed effect size when simulate the OC for various sample 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 400, 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.

Function provides two types of operating characteristics via simulation. If user specifies the value of ors and fixed_ss, function will calculate the design's power in terms of effect size. If user specifies the value of nmax and fixed_es, function will calculate the design's power in terms of sample size, and nmax is the upper limitation of sample size for the treatment and control groups at each stage, the lower limitation is 50, the default increment of the sequence is 10.

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

Examples

ors = matrix(c(1.5,1.5,1,1,1,1.5,1.5,1.1,1.1,1.1), nrow=2, ncol=5, byrow=TRUE)

get_oc_NPO(alpha = 0.05, pro_ctr = c(0.58,0.05,0.17,0.03,0.04,0.13),
           U = c(100,80,65,25,10,0), fixed_ss = 200, ors, ntrial = 5,
           method = "Frequentist")

set.seed(123)
get_oc_NPO(alpha = 0.05, pro_ctr = c(0.58,0.05,0.17,0.03,0.04,0.13),
           U = c(100,80,65,25,10,0), nmax = 100, fixed_es = c(1.5,1.3,1,1,1),
           ntrial = 5, method = "Frequentist")