2-Arm, Continuous Outcome, Repeated Measures - Patient Simulation

Shubham Lahoti

October 16, 2025

This example is related to the Integration Point: Response - Continuous Outcome with Repeated Measures. Click on the link for more information about this integration point.

Introduction

Repeated measures involve collecting multiple data points for the same variable from the same subjects across multiple time periods. This method provides insights into the subjects’ development and changes over time. In this context, a single subject can have multiple responses over time, and these responses can be correlated across different visits.

The following example illustrates how to integrate new patient outcome simulation (response) capabilities into East Horizon or East using R functions in the context of a two-arm trial with repeated measures.

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

CyneRgy::RunExample( "2ArmNormalRepeatedMeasuresResponseGeneration" )

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

RStudio Project File: 2ArmNormalRepeatedMeasuresResponseGeneration.Rproj

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

1. GenerateResponseDiffOfMeansRepeatedMeasures.R - The R function named GenRespDiffOfMeansRepMeasures in the file is used to generate the normal responses across multiple visits for 2-arm normal repeated measures design.

2. LibraryMASS.R - This file is used to load the library MASS to run the mvrnorm function.

Example 1 - Normal Endpoint

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

For a normal endpoint with repeated measures, the generation of responses primarily depends on the mean and standard deviation across all visits, as well as the correlation between these visits. This examples uses built-in input variables coming from East Horizon or East to simulate repeated measures data. It uses the MASS::mvrnorm function. This example does not use any user-defined parameters.

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.

Accessing Responses in Analysis

The data generation function returns a list object that includes multiple repeated response variables, each corresponding to a specified visit. These responses are stored as numeric vectors and are labeled sequentially as Response1, Response2 and so on, depending on the number of visits specified. These Response variables serve as both outputs of the data generation step and inputs for subsequent analyses. For the analysis, all the data is combined into a SimData object (list of variables), therefore each response can be accessed directly using the $ operator, for example: SimData$Response1, SimData$Response2, etc. This structure facilitates the handling of multiple outcomes in repeated measures analyses. For further details on how to incorporate these multiple responses into your analysis, refer to Integration Point: Analysis - Continuous Outcome with Repeated Measures.