Elucidating some common biases in randomized controlled trials using directed acyclic graphs.

Area of Science:

• Biostatistics
• Epidemiology
• Clinical Trials

Background:

• Randomized clinical trials (RCTs) are crucial for causal inference but often deviate from ideal conditions.
• Trial imperfections like noncompliance, unblinding, and drop-out can complicate causal effect estimation.
• Clearly defining the estimand is essential but challenging given these imperfections.

Purpose of the Study:

• To demonstrate the utility of directed acyclic graphs (DAGs) in understanding the impact of common RCT imperfections.
• To clarify the identifiability of intention-to-treat (ITT), total treatment effect (TTE), and physiological treatment effect (PTE) under various imperfections.
• To establish conditions under which the PTE is identifiable.

Main Methods:

• Utilized directed acyclic graphs (DAGs) to model causal relationships and identify confounding.
• Analyzed the identifiability of different treatment effect estimands (ITT, TTE, PTE) in the presence of noncompliance, unblinding, and drop-out.
• Applied DAG-based causal inference principles to randomized trial settings.

Main Results:

• DAGs effectively illustrate how noncompliance, unblinding, and drop-out affect the identifiability of ITT, TTE, and PTE.
• The study highlights specific scenarios where these common imperfections hinder the estimation of causal effects.
• Identified that the physiological treatment effect is generally not identifiable without perfect compliance, no drop-out, and maintained blinding.

Conclusions:

• Directed acyclic graphs are valuable tools for navigating the complexities of causal inference in imperfect randomized trials.
• Understanding the identifiability of specific estimands is critical for accurate causal effect estimation.
• The physiological treatment effect requires ideal trial conditions for accurate identification.