Identifying and avoiding design related biases in observational studies using the target trial framework.

Abstract

Observational studies are necessary to provide evidence to inform decision making in the absence of a relevant randomised trial. Although commonly criticised for potential problems due to confounding bias, design related biases in observational studies are often overlooked yet highly prevalent. Design related biases occur because of decisions made by researchers during analyses of observational data. Common design related biases include bias related to selection and treatment misclassification, resulting from misalignment of eligibility ascertainment, treatment strategy assignment, and start of follow-up. Conceptualising the analysis of observational data to estimate the causal effects of interventions as an attempt to explicitly emulate a target trial can help avoid design related biases, so that investigators can instead focus on data related biases (eg, confounding, measurement error) not directly addressed by the framework. Target trial emulation may also help readers appraise an observational study when transparently reported. This article aims to help readers of observational studies identify and avoid design related biases to support the use of observational evidence to inform clinical and policy decision making.