tteICE: An R Package for Estimating Treatment Effects on Time-to-Event Outcomes with Intercurrent Events in Two-Arm Trials

2026-09-20

AI Summary

该文介绍R包tteICE，通过实施五种策略解决临床试验中因并发事件导致的时间到事件结果的治疗效果评估问题。

Abstract

Evaluating treatment effects in clinical trials with time-to-event outcomes is complicated by intercurrent events (ICEs), e.g., treatment discontinuation or competing events. While the ICH E9 (R1) addendum outlines five strategies to address these challenges, accessible software implementing these methods is limited. This article introduces the \pkg{tteICE} package for \proglang{R}, which implements five strategies to facilitate valid causal inference in the presence of ICEs. \pkg{tteICE} enables researchers to either nonparametrically or semiparametrically efficiently estimate potential cumulative incidence functions under both treatment and control conditions. The treatment effect is assessed by contrasting these functions, with statistical uncertainty quantified using pointwise confidence intervals and $p$-values. The package supports data from randomized controlled trials and observational studies, including settings with competing risks and semi-competing risks. To reduce barriers for applied researchers, \pkg{tteICE} features integrated plotting functions and an accompanying interactive Shiny application that provides a user-friendly graphical interface with step-by-step instructions.

Problem

• intercurrent events
• treatment effects
• time-to-event outcomes

Innovation

• tteICE
• Intercurrent Events
• Causal Inference
• Cumulative Incidence Functions
• Shiny Application