Integration Point: Initialization

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Description

The Initialization integration point allows you to specify an optional function that will run before any other user-defined functions during simulations. This function can serve various purposes, such as:

Setting a seed for reproducibility in the R environment.
Loading required R packages or libraries.
Setting global variables.
Setting the working directory.
Sourcing an additional file.
Performing other initial setup tasks.

Availability

East Horizon Explore

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

	Time to Event	Binary	Continuous	Continuous with repeated measures	Count	Composite	Dual TTE-TTE	Dual TTE-Binary
Two Arm Confirmatory	✅	✅	✅	✅	❌	❌	✅	✅
Multiple Arm Confirmatory	🔜	✅	✅	-	-	-	-	-
Dose Finding	-	-	❌	-	-	-	-	-

Legend

Icon	Meaning
✅	Available
❌	Not available
🔜	Coming soon

East Horizon Design

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

Test	Study Objective	Endpoint	Availability
Single Mean (One Arm Design)	One Arm Exploratory/Confirmatory	Continuous	❌
Mean of Paired Differences (Paired Design)	One Arm Exploratory/Confirmatory	Continuous	❌
Mean of paired Ratios (Paired Design)	One Arm Exploratory/Confirmatory	Continuous	❌
Single Proportion (One Arm Design)	One Arm Exploratory/Confirmatory	Binary	❌
Simon’s Two Stage (One Arm Design)	One Arm Exploratory/Confirmatory	Binary	❌
Logrank Weibull Distribution (One Arm Design)	One Arm Exploratory/Confirmatory	Time to Event	❌
Parametric Weibull Distribution (One Arm Design)	One Arm Exploratory/Confirmatory	Time to Event	❌
Logrank Exponential Distribution (One Arm Design)	One Arm Exploratory/Confirmatory	Time to Event	❌
Single Poisson Rate (One Arm Design)	One Arm Exploratory/Confirmatory	Count	❌
Difference of Means (Parallel Design)	Two Arm Confirmatory	Continuous	✅
Ratio of Means (Parallel Design)	Two Arm Confirmatory	Continuous	❌
Difference of Means (Crossover Design)	Two Arm Confirmatory	Continuous	❌
Ratio of Means (Crossover Design)	Two Arm Confirmatory	Continuous	❌
Difference of Proportions (Parallel Design)	Two Arm Confirmatory	Binary	✅
Ratio of Proportions (Parallel Design)	Two Arm Confirmatory	Binary	✅
Odds Ratio of Proportions (Parallel Design)	Two Arm Confirmatory	Binary	✅
Fisher’s Exact (Parallel Design)	Two Arm Confirmatory	Binary	❌
Logrank Test Given Accrual Duration and Accrual Rates (Parallel Design)	Two Arm Confirmatory	Time to Event	✅
Logrank Test Given Accrual Duration and Study Duration (Parallel Design)	Two Arm Confirmatory	Time to Event	✅
Logrank Test Given Accrual Duration and Accrual Rates (Population Enrichment)	Two Arm Confirmatory	Time to Event	❌
Ratio of Poisson Rates (Parallel Design)	Two Arm Confirmatory	Count	❌
Ratio of Negative Binomial Rates (Parallel Design)	Two Arm Confirmatory	Count	❌
Win Ratio (Parallel Design)	Two Arm Confirmatory	Composite	❌
MAMS Difference of Means (Pairwise Comparisons to Control)	Multiple Arm Confirmatory	Continuous	✅
MAMS Difference of Means: Combining P-Values (Pairwise Comparisons to Control)	Multiple Arm Confirmatory	Continuous	✅
MAMS Difference of Proportions (Pairwise Comparisons to Control)	Multiple Arm Confirmatory	Binary	✅
MAMS Difference of Proportions: Combining P-Values (Pairwise Comparisons to Control)	Multiple Arm Confirmatory	Binary	✅
MAMS Logrank (Pairwise Comparisons to Control)	Multiple Arm Confirmatory	Time to Event	🔜
MAMS Logrank: Combining P-Values (Pairwise Comparisons to Control)	Multiple Arm Confirmatory	Time to Event	🔜

East

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

Test	Number of Samples	Endpoint	Availability
Difference of Means (Parallel Design)	Two Samples	Continuous	✅
Difference of Proportions (Parallel Design)	Two Samples	Discrete	✅
Ratio of Proportions (Parallel Design)	Two Samples	Discrete	✅
Odds Ratio of Proportions (Parallel Design)	Two Samples	Discrete	✅
Logrank Test Given Accrual Duration and Accrual Rates (Parallel Design)	Two Samples	Survival	✅
Logrank Test Given Accrual Duration and Study Duration (Parallel Design)	Two Samples	Survival	✅
Chi-Square for Specified Proportions in C Categories (Single Arm Design)	Many Samples	Discrete	✅
Two Group Chi-Square for Proportions in C Categories (Parallel Design)	Many Samples	Discrete	✅
Multiple Looks - Combining P-Values (Pairwise Comparisons to Control - Difference of Means)	Many Samples	Continuous	✅
Multiple Looks - Combining P-Values (Multiple Pairwise Comparisons to Control - Difference of Proportions)	Many Samples	Discrete	✅
Multiple Looks - Combining P-Values (Pairwise Comparisons to Control - Logrank Test)	Many Samples	Survival	✅

Instructions

In East Horizon (Explore and Design)

You can set up an initialization function when creating a new project by navigating to the Initialize R Simulations option under the Plan section.

Follow these steps (click to expand/collapse):

Choose Initialize R Simulations in the Plan section when setting up a new Project.
Turn on the switch to enable the feature.
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.
Specify the function name you want to initialize. 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.

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

In East

You can set up an initialization function by navigating to the Initialize R Environment task of the User Defined R Function tab of a Simulation Input window, after including the option. For some tests, the option will be available in the Treatment Selection tab.

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 Initialize R Environment.
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:

For the Multiple Looks - Combining P-Values (MAMS) tests, the option will be available when checking the box Use R for Treatment Selection in the Treatment Selection tab of the Simulation Input window. 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
Seed	Integer	Randomization seed set by the engine.
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:

Output	Type	Description
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.

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

Init <- function( Seed = NULL, UserParam = NULL )
{
  # Do something, for example set the seed
  set.seed( 42 )
  # If you want the user to set the seed using East Horizon's or East's interface, you could use set.seed( UserParam$seed ) 
  
  # Error handling (no error)
  nError <- 0

  return( as.integer( nError ) )
}

Examples

Used to load libraries in the following examples:

Gabriel Potvin

October 31, 2025