Randomization of Subjects

Shubham Lahoti, Gabriel Potvin, Anoop Singh Rawat

October 31, 2025

This example is related to the Integration Point: Randomization. Click the link for setup instructions, variable details, and additional information about this integration point.

To try this example, create a new project in East Horizon using the following configuration:

• Study objective:
• Two Arm Confirmatory for Examples 1, 2, 3
• Multiple Arm Confirmatory for Example 4
• Number of endpoints: Any
• Endpoint type: Any
• Task: Any

Introduction

The following examples illustrate how to integrate new custom randomization methods and control how subjects are assigned to designated arms into East Horizon or East using R functions in the context of a two-arm trial.

Once CyneRgy is installed, you can load this example in RStudio with the following commands

CyneRgy::RunExample( "RandomizeSubjects" )

Running the command above will load the RStudio project in RStudio.

RStudio Project File: RandomizeSubjects.Rproj

In the R directory of this example you will find the following R files:

1. RandomizationSubjectsUsingUniformDistribution.R - Contains a function named RandomizationSubjectsUsingUniformDistribution to demonstrate the R code necessary for Example 1 as described below.

2. RandomizationSubjectsUsingSampleFunctionInR.R - Contains a function named RandomizationSubjectsUsingSampleFunctionInR to demonstrate the R code necessary for Example 2 as described below.

3. BlockRandomizationSubjectsUsingRPackage.R - Contains a function named BlockRandomizationSubjectsUsingRPackage to demonstrate the R code necessary for Example 3 as described below.

4. LoadrandomizeR.R - This file is used to install the randomizeR package for execution of Block Randomization in R.

5. RandomizeSubjectsAcrossMultipleArms.R - Contains a function named RandomizeSubjectsAcrossMultipleArms to demonstrate the R code necessary for Example 4 as described below.

Example 1 - Randomize Subjects Usings Uniform Distribution

This example is related to this R file: RandomizationSubjectsUsingUniformDistribution.R

The R function RandomizationSubjectsUsingUniformDistribution randomly assigns subjects to one of two arms using a uniform distribution.

This function does not use any user-defined parameters, but uses parameters specified in East Horizon. Refer to the table below for more information.

Name of the parameter in East Horizon	Where to find the parameter in East Horizon	Name of the variable in the R script
Allocation Ratio	Design Card	$AllocRatio$

Steps:

1. Generate a random number $u$ from a $Uniform(0,1)$ distribution.
2. Define $p$ as the allocation fraction for the control arm, with $1 - p$ as the allocation fraction for the treatment arm.
3. If $u \leq p$, assign the subject to the control arm; otherwise, assign them to the treatment arm.
4. Ensure that the total sample size equals the sum of subjects in the control and treatment arms.

The figure below illustrates where this example fits within the R integration points of Cytel products, accompanied by a flowchart outlining the general steps performed by the R code.

Example 2 - Randomize Subjects Using Sample Function

This example is related to this R file: RandomizationSubjectsUsingSampleFunctionInR.R

The R function RandomizationSubjectsUsingSampleFunctionInR uses the sample() function in R to randomly assign patients to the control and treatment arms.

This function does not use any user-defined parameters, but uses parameters specified in East Horizon. Refer to the table below for more information.

Name of the parameter in East Horizon	Where to find the parameter in East Horizon	Name of the variable in the R script
Allocation Ratio	Design Card	$AllocRatio$

Steps:

1. Let $p$ be the allocation fraction for the control arm, with $1 - p$ as the allocation fraction for the treatment arm.
2. Compute the expected sample sizes (rounded) for both arms using the allocation fraction and total sample size.
3. Randomly assign subject indices to the control and treatment arms using the sample() function in R.
4. Create a vector of zeros with size equal to the total number of subjects ($NumSub$), then replace the zeros with ones for indices corresponding to the treatment arm.

The figure below illustrates where this example fits within the R integration points of Cytel products, accompanied by a flowchart outlining the general steps performed by the R code.

Example 3 - Randomize Subjects Using randomizeR Package

This example is related to this R file: BlockRandomizationSubjectsUsingRPackage.R

The function BlockRandomizationSubjectsUsingRPackage.R uses the randomizeR::pbrPar() function from the randomizeR package to perform block randomization.

In small-sample studies, imbalances between groups can be minimized by using restricted randomization. This approach ensures that treatment groups are allocated in the desired proportions while maintaining randomness.

The permuted block randomization technique divides study participants into blocks, within which subjects are randomly assigned to treatment groups. This method ensures that the desired allocation proportions are exactly maintained within each block while keeping the order of assignments random.

Refer to the table below for the definitions of the user-defined parameters used in this example.

User parameter	Definition
BlockSize1	Size of the first block for the randomization process.
BlockSize2	Size of the second block for the randomization process.
…	Block sizes for additional blocks follow the same format, incrementing the block number (e.g., BlockSize3, BlockSize4, etc.).

This function also uses parameters specified in East Horizon. Refer to the tables below for more information.

Name of the parameter in East Horizon	Where to find the parameter in East Horizon	Name of the variable in the R script
Allocation Ratio	Design Card	$AllocRatio$

The figure below illustrates where this example fits within the R integration points of Cytel products, accompanied by a flowchart outlining the general steps performed by the R code.

Example 4 - Randomize Subjects Across Multiple Arms

This example is related to this R file: RandomizeSubjectsAcrossMultipleArms.R

The function RandomizeSubjectsAcrossMultipleArms demonstrates how to perform randomization when a clinical trial involves more than two arms, such as a control group and multiple experimental treatment groups.

This function dynamically handles any number of arms and allocates subjects according to specified allocation ratios. It extends the logic of the two-arm randomization examples to generalize across an arbitrary number of treatment arms.

The function first converts allocation ratios into fractions summing to one, then determines the number of subjects per arm according to these proportions. Any remaining subjects (due to rounding) are distributed to the arms with the largest fractional parts to ensure that the total sample size is preserved. A treatment ID vector is then created (0 for control, 1…N for treatment arms), randomly shuffled, and returned.

This function does not use any user-defined parameters, but uses parameters specified in East Horizon. Refer to the table below for more information.

Name of the parameter in East Horizon	Where to find the parameter in East Horizon	Name of the variable in the R script
Allocation Ratio for Control Arm	Design Card	$AllocRatio[ 1 ]$
Allocation Ratio for Arm 1	Design Card	$AllocRatio[ 2 ]$
Allocation Ratio for Arm 2	Design Card	$AllocRatio[ 3 ]$
…

The figure below illustrates where this example fits within the R integration points of Cytel products, accompanied by a flowchart outlining the general steps performed by the R code.