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

TL;DR

This study compares treatment effect estimates from sequential nested trial emulations to single point trials using simulations, highlighting differences in causal estimands especially when disease severity modifies treatment effects.

Contribution

It provides a simulation-based comparison of SNT emulations and single point trials, clarifying the causal estimands and their interpretability in different scenarios.

Findings

When disease severity does not modify effects, estimates are similar across methods.

Disease severity modification causes divergence in estimates between SNT and single point trials.

SNT emulations target different populations, complicating causal interpretation.

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.