Potential biases in observational infectious disease studies: examples using directed acyclic graphs

Clinical Microbiology and Infection, vol. 32, pp. 1603–1610

Abstract

BACKGROUND: Bias is a systematic error that threatens the (internal) validity of research. Observational studies in infectious diseases are especially prone to bias because they are typically nonrandomized and often rely on routinely collected or retrospective data. OBJECTIVES: To provide an overview of common biases in observational infectious diseases research, introduce the concept of directed acyclic graphs (DAGs), and outline practical approaches for bias identification and mitigation. SOURCES: This narrative review draws on biases commonly encountered by the authors as editors, reviewers, and clinicians, supplemented by PubMed searches and recent reviews from other medical fields. CONTENT: The paper introduces DAGs as a conceptual tool to identify bias. Biases are grouped into selection, time-related, and information bias. Examples from published infectious diseases studies are given together with strategies to detect and reduce bias. IMPLICATIONS: Intended as an introductory guide for clinicians, investigators, and reviewers, this paper aimed to improve critical appraisal and study design, while acknowledging it does not cover all possible biases.

Authors 3

1. Erlangga Yusuf corresponding

   Erasmus MC

   Affiliation as printed

   Department of Medical Microbiology and Infectious Diseases, Erasmus MC, Rotterdam, The Netherlands. Electronic address: angga.yusuf@gmail.com

2. Oksana Martinuka

   University of Freiburg

   Affiliation as printed

   Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre - University of Freiburg, Freiburg, Germany

3. Frits Richard Rosendaal Aachen Department of Clinical Epidemiology

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands