Target Trial Emulation with the R Package TTE: A Tutorial and Methodological Guide

2026-08-02

AI Summary

This study addresses the susceptibility of causal effect estimation in observational studies to bias by proposing a systematic framework based on target trial emulation. By rigorously specifying eligibility criteria, treatment assignment, time zero, and follow-up rules to emulate a randomized controlled trial design, the approach integrates advanced statistical methods—including inverse probability weighting, weighted discrete-time survival models, model standardization, competing risk analysis, and cluster bootstrap at the individual level—into an end-to-end R implementation. The framework supports comparative analyses of both intention-to-treat and per-protocol effects and demonstrates its validity and practicality through two synthetic case studies, successfully estimating relative risks, absolute risks, and cumulative incidence functions.

Abstract

Target trial emulation structures observational causal analyses around the protocol of an ideal randomized trial. By aligning eligibility, treatment assignment, time zero, and follow-up, it can reduce avoidable biases, but implementation still requires coordinated decisions about data construction, inverse probability weighting, diagnostics, outcome models, standardization, competing risks, and uncertainty estimation. This article provides a self-contained methodological guide and practical tutorial for TTE, an R package for target trial emulations with longitudinal observational data. We describe target trial protocols, intention-to-treat and per-protocol estimands, identification assumptions, baseline and person-period data structures, temporal ordering for longitudinal weights, stabilized treatment and censoring weights, weight truncation, balance and effective-sample-size diagnostics, weighted pooled discrete-time survival models, model-based standardization, competing-risk analysis, weighted Kaplan-Meier and Aalen-Johansen estimation, and cluster bootstrap at the original-individual level. Two fully synthetic examples illustrate end-to-end workflows: sodium-glucose cotransporter 2 inhibitor versus dipeptidyl peptidase-4 inhibitor initiation with all-cause death, and sequentially nested angiotensin receptor blocker versus calcium channel blocker trials with heart-failure hospitalization and competing death. The examples show how to obtain and diagnose estimates in R and interpret relative hazards, absolute risks, cumulative incidence, and differences between intention-to-treat and per-protocol effects.

Problem

• target trial emulation
• observational data
• causal inference
• longitudinal analysis
• bias reduction

Innovation

• target trial emulation
• inverse probability weighting
• competing risks
• discrete-time survival models
• cluster bootstrap