Matrix tools to identify and evaluate quantitative bias analysis methods for summary-level epidemiologic data

Quantitative bias analysis (QBA) methods can be used to estimate the potential impact of systematic biases on effect estimates from observational studies. However, QBA methods remain underused in applied health research, partly due to difficulties in selecting methods that are suitable for specific study designs. Therefore, we developed structured applicability matrices to help identify and evaluate QBA methods suitable for use with summary-level data across diverse study settings. We identified summary-level data QBA methods through a systematic review of MEDLINE, Embase, Scopus, and Web of Science, and classified each method by bias type: unmeasured confounding, information bias, or selection bias. For each bias type, we developed an applicability matrix that categorized methods as “Applicable”, “Maybe Applicable”, or “Not Applicable”, based on study design, required bias parameters, data format for the exposure, confounder, and outcome, type of effect estimate, output produced, and any stated assumptions or implementation requirements. To demonstrate use of the matrices, we applied them to three published observational studies, each presenting a distinct bias challenge. We developed a set of three QBA applicability matrices based on 55 QBA methods for summary-level data (unmeasured confounding [ n = 28], information bias [ n = 21], and selection bias [ n = 6]). Across the three QBA applicability matrices, most methods were classified as “Applicable” for cohort studies ( n = 35) and case-control studies ( n = 40), while only four methods were “Applicable” for meta-analyses of observational studies. QBA methods were consistently classified as “Applicable” for studies with binary exposures ( n = 51) or outcomes ( n = 51). However, fewer methods were “Applicable” for studies with polytomous exposures ( n = 13) or outcomes ( n = 6), as well as continuous exposures ( n = 7) or outcomes ( n = 7). Most QBA methods were classified as “Applicable” for studies reporting risk ratios ( n = 35) or odds ratios ( n = 41); in contrast, only 11 methods were “Applicable” for studies with rate ratios, and 9 methods were “Applicable” for those using hazard ratios for time-to-event outcomes. In the applied examples, the matrices successfully narrowed the set of potentially relevant methods and provided structured guidance for method evaluation and selection. The QBA applicability matrices offer a systematic framework for identifying methods suitable for summary-level data across diverse study contexts. By reducing barriers to method selection and implementation, they aim to enhance the rigor of causal inference in observational research. Not applicable.