Biases in Randomized Trials: A Conversation Between Trialists and Epidemiologists

Area of Science:

• Epidemiology
• Biostatistics
• Clinical Trials

Background:

• Trialists and epidemiologists use different terms for study biases.
• Many biases share similar underlying structures across study types.

Purpose of the Study:

• To bridge the terminological gap between trialists and epidemiologists regarding biases.
• To clarify bias structures using causal diagrams and translate them into epidemiologic terms.
• To emphasize the need for explicit inferential goals in bias assessment.

Main Methods:

• Utilizing causal diagrams to represent bias structures in randomized trials (Cochrane methodology).
• Translating bias structures into standard epidemiologic terms: confounding, selection bias, and measurement bias.
• Analyzing the necessity of defining the intention-to-treat or per-protocol effect for bias assessment.

Main Results:

• Causal diagrams provide a unified framework for understanding biases in different study designs.
• A clear translation between terminologies facilitates better comprehension of bias.
• Defining the specific effect estimate (e.g., intention-to-treat) is crucial for evaluating bias risk.

Conclusions:

• Adopting a common structural approach to bias, aided by causal diagrams, can enhance interdisciplinary communication.
• Awareness of shared bias concepts and terminology improves the rigor of causal inference.
• This unified perspective aids in more accurate risk of bias assessments in both randomized and observational studies.