Quantitative bias analysis methods for summary-level epidemiologic data in the peer-reviewed literature: a systematic review

Objectives

Quantitative bias analysis (QBA) methods evaluate the impact of biases arising from systematic errors on observational study results. This systematic review aimed to summarize the range and characteristics of QBA methods for summary-level data published in the peer-reviewed literature.

Study Design and Setting

We searched MEDLINE, Embase, Scopus, and Web of Science for English-language articles describing QBA methods. For each QBA method, we recorded key characteristics, including applicable study designs, bias(es) addressed; bias parameters, and publicly available software. The study protocol was preregistered on the Open Science Framework (https://osf.io/ue6vm/).

Results

Our search identified 10,249 records, of which 53 were articles describing 57 QBA methods for summary-level data. Of the 57 QBA methods, 53 (93%) were explicitly designed for observational studies, and 4 (7%) for meta-analyses. There were 29 (51%) QBA methods that addressed unmeasured confounding, 19 (33%) misclassification bias, 6 (11%) selection bias, and 3 (5%) multiple biases. Thirty-eight (67%) QBA methods were designed to generate bias-adjusted effect estimates and 18 (32%) were designed to describe how bias could explain away observed findings. Twenty-two (39%) articles provided code or online tools to implement the QBA methods.

Conclusion

In this systematic review, we identified a total of 57 QBA methods for summary-level epidemiologic data published in the peer-reviewed literature. Future investigators can use this systematic review to identify different QBA methods for summary-level epidemiologic data.

Plain Language Summary

Quantitative bias analysis (QBA) methods can be used to evaluate the impact of biases on observational study results. However, little is known about the full range and characteristics of available methods in the peer-reviewed literature that can be used to conduct QBA using information reported in manuscripts and other publicly available sources without requiring the raw data from a study. In this systematic review, we identified 57 QBA methods for summary-level data from observational studies. Overall, there were 29 methods that addressed unmeasured confounding, 19 that addressed misclassification bias, six that addressed selection bias, and three that addressed multiple biases. This systematic review may help future investigators identify different QBA methods for summary-level data.