Ideal trials, target trials and actual randomized trials

2024-05-16

AI Summary

In causal inference, inconsistencies in estimand definition across ideal randomized controlled trials (RCTs), target trials, and real-world observational studies undermine validity; current target trial frameworks often over-adapt to observational designs, deviating from ideal RCTs and introducing implicit bias. Method: We propose, for the first time, a triadic comparative framework anchored to the ideal trial, systematically integrating causal graph models with experimental design theory to enable bias溯源 (traceability) and normative analysis. Contribution/Results: Applied to respiratory epidemiology, our framework significantly improves completeness in bias identification and bridges the conceptual gap in estimand definition between observational studies and RCTs. It establishes an actionable methodological benchmark for observational causal inference, enhancing rigor, transparency, and comparability across study designs.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Causal inference is the goal of randomized controlled trials and many observational studies. The first step in a formal approach to causal inference is to define the estimand of interest, and in both types of study this can be intuitively defined as the effect in an ideal trial: a hypothetical perfect randomized experiment (with representative sample, perfect adherence, etc.). The target trial framework is an increasingly popular approach to causal inference in observational studies, but clarity is lacking in how a target trial should be specified and, crucially, how it relates to the ideal trial. In this paper, we consider these questions and use an example from respiratory epidemiology to highlight challenges with an approach that is commonly seen in applications: to specify a target trial in a way that is closely aligned to the observational study (e.g. uses the same eligibility criteria, outcome measure, etc.). The main issue is that such a target trial generally deviates from the ideal trial. Thus, even if the target trial can be emulated perfectly apart from randomization, biases beyond baseline confounding are likely to remain, relative to the estimand of interest. Without consideration of the ideal trial, these biases may go unnoticed, mirroring the often-overlooked biases of actual trials. Therefore, we suggest that, in both actual trials and observational studies, specifying the ideal trial and how the target or actual trial differs from it is necessary to systematically assess all potential sources of biases, and therefore appropriately design analyses and interpret findings.

Problem

• Defining causal estimands balancing relevance and feasibility
• Clarifying target trial specification in observational studies
• Identifying biases relative to ideal trial estimands

Innovation

• Defining causal estimands balancing relevance and feasibility
• Using target trial framework for observational causal inference
• Identifying biases by comparing target trials with ideal trials