Integration Point: Randomization

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Description

The Randomization integration point allows you to incorporate custom randomization methods into Cytel products. By specifying a function, you can control how subjects are assigned to designated arms based on your specific requirements, replacing the default complete randomization approach. For example, you can implement advanced randomization techniques such as block randomization, stratified randomization, or cluster randomization.

Availability

East Horizon Explore

This integration point is available in East Horizon Explore for the following study objectives and endpoint types:

Two Arm Confirmatory: continuous, continuous with repeated measures, binary, and time-to-event outcomes.
Two Arm Confirmatory: dual time-to-event/time-to-event, dual time-to-event/binary outcomes. Coming soon
Multiple Arm Confirmatory: continuous and binary outcomes. Coming soon

East Horizon Design Coming soon

This integration point will be available in East Horizon Design in the future.

East

This integration point is available in East for the following tests (click to expand/collapse):

Design, Continuous Endpoint
- Two-Sample Test, Parallel Design, Difference of Means (MN-2S-DI)
Design, Discrete Endpoint
- Two-Sample Test, Parallel Design
  - Difference of Proportions (PN-2S-DI)
  - Ratio of Proportions (PN-2S-RA)
  - Odds Ratio of Proportions (PN-2S-OR)
Design, Survival Endpoint
- Two-Sample Test, Parallel Design
  - Logrank Given Accrual Duration and Accrual Rates (SU-2S-LRAR)
  - Logrank Given Accrual Duration and Study Duration (SU-2S-LRSD)

Instructions

In East Horizon Explore

You can set up a response function in East Horizon Explore under Randomization Method in a Design Card while creating or editing an Input Set.

Follow these steps (click to expand/collapse):

Select User Specified-R from the dropdown in the Randomization Method field in the Design Card.
Browse and select the appropriate R file (filename.r) from your computer, or use the built-in R Code Assistant to create one. This file should contain function(s) written to perform various tasks to be used throughout your Project.
Choose the appropriate function name. If the expected function is not displaying, then check your R code for errors.
Set any required user parameters (variables) as needed for your function using + Add Variables.
Continue creating your project by specifying scenarios for patient Response, Enrollments, etc.

For a visual guide of where to find the option, refer to the screenshot below:

Screenshot of East Horizon

In East

You can set up a randomization function in East by navigating to the Randomize Subjects task of the User Defined R Function tab of a Simulation Input window, after including the option.

Follow these steps (click to expand/collapse):

Choose the appropriate test in the Design tab.
If you see the Design Input window, compute the scenario using the Compute button, save the design using the Save in Workbook button, then navigate to the Simulation Input window by clicking on the Simulate Design button under Library.
Click on the Include Options button on the top right corner of the Simulation Input window and select both Accrual / Dropouts and User Defined R Function.
In the tab User Defined R Function, a list of tasks will appear. Place your cursor in the File Name field for the task Randomize Subjects.
Click on the button Browse… to select the appropriate R file (filename.r) from your computer. This file should contain function(s) written to perform various tasks to be used throughout your Project.
Specify the function name you want to initialize. To copy the function’s name from the R script, click on the button View.
Set any required user parameters (variables) as needed for your function using the button Add/Edit Variables.
Continue setting up your project.

For a visual guide of where to find the option, refer to the screenshot below:

Input Variables

When creating a custom R script, you can optionally use certain variables provided by East Horizon’s or East’s engine itself. These variables are automatically available and do not need to be set by the user, except for the UserParam variable. Refer to the table below for the variable that is available for this integration point.

Variable	Type	Description
NumSub	Integer	Number of subjects in the trial.
NumArms	Integer	Number of arms in the trial ﴾including placebo/control, and experimental﴿.
AllocRatio	Vector of Numeric	Vector of length `NumArm - 1`, indicating the expected allocation ratios for each treatment arm relative to the control arm ( $n_{t_i}/n_c$ ).
UserParam	List	Contains all user-defined parameters specified in East Horizon’s or East’s interface (refer to the Instructions section). To access these parameters in your R code, use the syntax: `UserParam$NameOfTheVariable`, replacing `NameOfTheVariable` with the appropriate parameter name.

Expected Output Variable

East Horizon expects an output of a specific type. Refer to the table below for the expected output for this integration point:

Type	Description
List	A named list containing `TreatmentID` and `ErrorCode`.

Expected Members of the Output List

Members	Type	Description
TreatmentID	Vector of Integer	Vector of length equal to the number of subjects, containing the allocation indices for all subjects: – `0`: Control arm. – `1`: First experimental arm. – etc.
ErrorCode	Integer	Optional. Can be used to handle errors in your script: – `0`: No error. – `Positive Integer`: Nonfatal error, the current simulation will be aborted, but the next simulation will proceed. – `Negative Integer`: Fatal error, no further simulations will be attempted.

Minimal Template

Your R script could contain a function such as this one, with a name of your choice. All input variables must be declared, even if they are not used in the script. We recommend always declaring UserParam as a default NULL value in the function arguments, as this will ensure that the same function will work regardless of whether the user has specified any custom parameters in the interface.

Randomize <- function( NumSub, NumArms, AllocRatio, UserParam = NULL )
{
  nError                <- 0 # Error handling (no error)
  vTreatmentArmIndex    <- rep( 0, NumSub ) # Initializing response array to 0: in that case all patients are in the control group
  
  # Write the actual code here.
  # Store the generated allocation indices in a vector called vTreatmentArmIndex.

  return( list( TreatmentID = as.Integer( vTreatmentArmIndex ), ErrorCode = as.integer( nError ) ) )
}

A detailed template with step-by-step explanations is available here: Randomization.R

Examples

Explore the following examples for more context:

2-Arm - Randomization of Subjects

Gabriel Potvin

January 16, 2025