Multiple Arm, Analysis

Gabriel Potvin and Anoop Singh Rawat

May 27, 2026

This example is related to the Integration Point: Analysis. 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: Multiple Arm Confirmatory
• Number of endpoints: Single Endpoint
• Endpoint type:
• Continuous Outcome for Example 1
• Binary Outcome for Example 2
• Time-to-Event Outcome for Example 3
• Task: Explore or Design

Note: This example is compatible with both Fixed Sample and Group Sequential statistical designs. The R code automatically detects whether interim look information (LookInfo) is available and adjusts the analysis parameters accordingly.

Introduction

The following examples illustrate how to integrate new analysis capabilities into East Horizon using R functions in the context of multi-arm clinical trials. Each example demonstrates a different type of statistical endpoint (continuous, time-to-event, and binary), using standard R functions and a Bonferroni adjustment to handle multiple comparisons.

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

1. AnalyzeMultiArmUsingTTestBonferroni.R - Performs pairwise t-tests for continuous endpoints with Bonferroni adjustment.

2. AnalyzeMultiArmUsingPropTestBonferroni.R - Performs proportion tests for binary endpoints with Bonferroni adjustment.

3. AnalyzeMultiArmUsingLogrankTestBonferroni.R - Performs log-rank tests for time-to-event endpoints with Bonferroni adjustment.

Example 1 - Using the t.test() Function with Bonferroni Adjustment (Continuous Outcome)

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

This example uses R’s built-in t.test() function to compare continuous outcomes between each treatment arm and the control arm. The function computes individual p-values for each comparison, applies a Bonferroni adjustment to control the family-wise error rate, and then checks whether any adjusted p-values cross the efficacy boundaries defined by East Horizon.

At each interim or final analysis, only active arms are tested (based on IsArmPresent). The analysis is compatible with both fixed and group sequential designs.

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 - Using the prop.test() Function with Bonferroni Adjustment (Binary Outcome)

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

This example analyzes binary outcomes using R’s base prop.test() function. The test compares the proportion of responders in each treatment arm against the control arm, computes p-values for each test, and applies Bonferroni correction to account for multiple arms. Adjusted p-values are compared against efficacy boundaries to decide whether any arm demonstrates a statistically significant treatment effect.

The analysis dynamically accounts for dropped arms and adapts to both fixed and group sequential designs.

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 - Using the survival::survdiff() Function with Bonferroni Adjustment (Time-to-Event Outcome)

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

For time-to-event outcomes, this example uses the survival::survdiff() function to perform a log-rank test between each treatment arm and the control arm. The log-rank statistic from each comparison is converted to a p-value, adjusted using Bonferroni correction, and evaluated against the efficacy boundary to determine if any treatment demonstrates a statistically significant improvement in survival.

This example can handle interim looks and automatically incorporates information fraction and efficacy boundary adjustments provided by LookInfo.

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.