get.ts.bin.int.oc: Get two sample binary interim OC

Description

Get two sample binary interim OC

Usage

get.ts.bin.int.oc(
  a.con = 1,
  b.con = 1,
  a.trt = 1,
  b.trt = 1,
  Delta.tv = 0.3,
  Delta.lrv = 0.2,
  tau.tv = 0.1,
  tau.lrv = 0.8,
  tau.ng = 0.65,
  go.thresh = 0.8,
  ng.thresh = 0.8,
  n.con = 40,
  n.trt = 40,
  n.int.c = c(10, 20, 30),
  n.int.t = c(10, 20, 30),
  DecisionTable = NULL,
  runs = 500,
  ControlRate = 0.2,
  TreatmentEffect = seq(0, 0.8, 0.1),
  include_nogo = TRUE
)

Value

dataframe of simulation results with the following columns: * **Effect** the difference between the control Rate and treatment rate. (TreatmentRate = ControlRate + Effect) * **run** the grouping variable by run. * **assessment** the assessment in order, the last assessment is the final analysis results. * **TreatmentEffect** The true response rate for the treatment group. (ControlRate is the same for all runs, it is not in the output, only the function input. We may want to change this for completeness, or include it in Param) * **TotalResponse** for control (_C) and treatment(_T) respectively the total number of responses observed at that particular assessment. This is the cumulative sum over the assessments. * **Decision** The decision at the assessment given the total response up to that point. Note that the decision is calculated differently depending the assessment, the last assessment uses final assessment calculation while the earlier assessments use the interim assessment calculations.

Arguments

a.con: alpha parameter for control
b.con: beta parameter for control
a.trt: alpha parameter for treatment
b.trt: beta parameter for treatment
Delta.tv: Base TPP
Delta.lrv: min TPP
tau.tv: study-end threshold for Base TPP
tau.lrv: study-end threshold for Min TPP
tau.ng: study-end threshold for NG
go.thresh: threshold for predictive probabilities at interim
ng.thresh: threshold for predictive probabilities at interim
n.con: final sample size for control
n.trt: final sample size for treatment
n.int.c: interim sample sizes for control
n.int.t: interim sample sizes for treatment
DecisionTable: results from get.ts.bin.int.dec
runs: number of simulation runs
ControlRate: assumed rate in the control group
TreatmentEffect: vector of treatment effects relative to control group.
include_nogo: logical

Examples

Run this code

my.ts.bin.int.oc <-  get.ts.bin.int.oc()
head(my.ts.bin.int.oc)

Run the code above in your browser using DataLab