Comparing causal estimands from sequential nested versus single point target trials: A simulation study

2026-01-28

AI Summary

This study investigates the discrepancies and interpretability challenges between causal effect estimates derived from simulated sequential nested trials (SNTs) and those from a single time-point target trial. Using Monte Carlo simulations, the authors compare two SNT designs—annual re-enrollment and treatment decision-based enrollment—across varying levels of disease severity to assess their performance in estimating treatment effects. The results show that when disease severity does not act as an effect modifier, SNT-based estimates align with those from the single time-point target trial. However, in the presence of effect modification, substantial divergence persists between the two approaches even after adequate adjustment for confounding. The findings highlight the complexity of defining the target population under effect heterogeneity in SNTs and caution against equating their causal estimates directly with those from conventional target trials.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Sequential nested trial (SNT) emulation is a powerful approach for maximizing precision and avoiding time-related biases. However, there exists little discussion about the implied causal estimands in comparison to a real-world single point trial. We used Monte Carlo simulation to compare treatment effect estimates from an SNT emulation that re-indexed patients annually and a SNT emulation with a treatment decision design to the estimates from a single point trial. We generated 5,000 cohorts of 5,000 people with 3 years of follow-up. For the single point trial, patients were randomized to initiate or not initiate treatment at Visit 1. For the SNT emulations, simulated patients could contribute up to two index dates. When disease severity did not modify the treatment effect, both SNT approaches returned treatment effect estimates identical to the single point trial. In the presence of treatment effect modification by disease severity, both SNT approaches returned treatment effect estimates that diverged from the single point trial even after confounding-adjustment. These findings underscore the difficulties of interpreting causal estimands from a SNT emulation: the target population does not correspond to a single time point trial. Such implications are important for communicating study results for evidence-based decision-making.

Problem

• causal estimands
• sequential nested trials
• single point trial
• treatment effect modification
• target trial emulation

Innovation

• sequential nested trial
• causal estimand
• treatment effect modification
• Monte Carlo simulation
• target trial emulation