Real-world studies (RWS) can more accurately reflect patient treatment outcomes and long-term prognosis in clinical practice, and they play an increasingly important role in drug effectiveness evaluation and regulatory decision-making. However, due to their non-randomized nature, RWS are susceptible to systematic biases - such as unmeasured (or uncontrolled) confounding bias, information bias (e.g., measurement error or misclassification), and selection bias - which may lead to deviations from the true effect and compromise the reliability of evidence and the rationality of policy decisions. Quantitative bias analysis (QBA) is a methodological approach used to assess the impact of bias on study results, enabling the quantification of the direction, magnitude, and uncertainty of such biases. To promote the standardized application of QBA in real-world research, this paper systematically reviews existing QBA methods and their applicable scenarios, aiming to provide methodological references and practical guidance for researchers and decision-makers in improving the interpretability and credibility of real-world evidence.
Conducting real-world studies inevitably faces the challenge of bias. Researchers need to employ appropriate bias analysis methods to determine whether the strength of causal associations derived from the study is distorted by potential biases. Traditional qualitative assessments and statistical methods often struggle to effectively quantify the impact of unknown or unmeasured residual biases on study conclusions. Quantitative bias analysis can address this by constructing quantitative models to quantify sources of bias (such as misclassification or unmeasured confounding) and systematically evaluate the direction and magnitude of the effect of residual biases on effect estimates after conventional analysis, thereby confirming the robustness of the estimates. This method has gained widespread international recognition for addressing key questions such as, "How much uncertainty might potential biases introduce?" and "How credible are the study conclusions in the presence of bias?" However, there is still a lack of systematic introduction to this method domestically. Therefore, this article will begin with the practical challenges of bias analysis in real-world studies and sequentially elaborate on the development, theoretical framework, implementation cases, and application of quantitative bias analysis in real-world research. It aims to systematically introduce the methodological characteristics of quantitative bias analysis, providing researchers with a methodological reference for addressing bias analysis issues.