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Select Experimental Treatments Better Than Control

SelectExpThatAreBetterThanCtrl.Rd

Select treatments that are higher than control or, if none are greater, select the treatment with the largest probability of response. At the interim analysis, select any treatment with a response rate that is higher than control for stage 2. If none of the treatments have a higher response rate than control, select the treatment with the largest probability of response. In the second stage, the randomization ratio will be 1:1 (experimental:control).

Usage

SelectExpThatAreBetterThanCtrl(
  SimData,
  DesignParam,
  LookInfo = NULL,
  UserParam = NULL
)

Arguments

SimData

Dataframe which consists of data generated in the current simulation.

DesignParam

List of Design and Simulation Parameters required to perform treatment selection.

LookInfo

List containing Design and Simulation Parameters, which might be required to perform treatment selection.

UserParam

A list of user-defined parameters in East or East Horizon. The default must be NULL.

Value

A list containing:

TreatmentID

A vector of experimental treatment IDs selected to advance, e.g., 1, 2, ..., number of experimental treatments.

AllocRatio

A vector of allocation ratios for the selected treatments relative to control.

ErrorCode

An integer indicating success or error status:

ErrorCode = 0

No error.

ErrorCode > 0

Nonfatal error, current simulation aborted but subsequent simulations will run.

ErrorCode < 0

Fatal error, no further simulations attempted.

Note

  • The length of TreatmentID and AllocRatio must be the same.

  • The allocation ratio for control is always 1, and AllocRatio values are relative to this. For example, an allocation value of 2 means twice as many participants are randomized to the experimental treatment compared to control.

  • The order of AllocRatio should match TreatmentID, with corresponding elements assigned their respective allocation ratios.

  • The returned vector includes only TreatmentID values for experimental treatments. For example, TreatmentID = c(0, 1, 2) is invalid because control (0) should not be included.

  • At least one treatment and one allocation ratio must be returned.

Examples

# Example 1: Assuming the allocation in the second part of the trial is 1:2:2 for Control:Experimental 1:Experimental 2
vSelectedTreatments <- c(1, 2)  # Experimental 1 and 2 both have an allocation ratio of 2.
vAllocationRatio    <- c(2, 2)
nErrorCode          <- 0
lReturn             <- list(TreatmentID = vSelectedTreatments, AllocRatio = vAllocationRatio, ErrorCode = nErrorCode)
return(lReturn)
#> $TreatmentID
#> [1] 1 2
#> 
#> $AllocRatio
#> [1] 2 2
#> 
#> $ErrorCode
#> [1] 0
#> 

# Example 2: Assuming the allocation in the second part of the trial is 1:1:2 for Control:Experimental 1:Experimental 2
vSelectedTreatments <- c(1, 2)  # Experimental 2 will receive twice as many as Experimental 1 or Control.
vAllocationRatio    <- c(1, 2)
nErrorCode          <- 0
lReturn             <- list(TreatmentID = vSelectedTreatments, AllocRatio = vAllocationRatio, ErrorCode = nErrorCode)
return(lReturn)
#> $TreatmentID
#> [1] 1 2
#> 
#> $AllocRatio
#> [1] 1 2
#> 
#> $ErrorCode
#> [1] 0
#>