Essential Concepts for Reducing Bias in Observational Studies

Abstract

Randomized controlled trials (RCTs) are the gold standard study design for clinical research, as prospective randomization, at least in theory, balances any differences that can exist between groups (including any differences not measured as part of the study) and isolates the studied treatment effect. Any remaining imbalances after randomization are attributable to chance. However, there are many barriers to conducting RCTs within pediatric populations, including lower disease prevalence, high costs, inadequate funding, and additional regulatory requirements. Researchers thus frequently use observational study designs to address many research questions. Observational studies, whether prospective or retrospective, do not involve randomization and thus have more potential for bias when compared with RCTs because of imbalances that can exist between comparison groups. If these imbalances are associated with both the exposure of interest and the outcome, then failure to account for these imbalances may result in a biased conclusion. Understanding and addressing differences in sociodemographic and/or clinical characteristics within observational studies are thus necessary to reduce bias. Within this Method/ology submission we describe techniques to minimize bias by controlling for important measurable covariates within observational studies and discuss the challenges and opportunities in addressing specific variables.