Structured tools to assessing quality and bias in Mendelian randomisation studies: an updated systematic review

Background

The growing use of Mendelian randomisation (MR) has heightened the need for rigorous quality and bias assessment tools. A previous systematic review included studies published up to July 2021 identified 14 structured instruments for conducting, evaluating, and reporting MR studies. However, methodological developments have accelerated in the years since.

Methods

We updated Spiga’s systematic review to include tools published between July 2021 and January 2025, applying the same search strategy and eligibility criteria. Two reviewers independently screened articles, extracted data, and mapped tool content to bias domains.

Results

We identified 15 additional articles, bringing the total to 29 tools. Of these, 19 provided structured evaluation tools. 12 of the 19 evaluation tools were newly added in the present review, which addressed broader methodological domains beyond core instrumental variable assumptions, including genetic instrument selection, population stratification, sensitivity analyses, and dataset considerations. However, substantial variation in bias domains, structure, and scoring methods across tools persists. Key gaps remain in the assessment of linkage disequilibrium, missing data, and dynastic effects.

Conclusions

While the number of structured tools has increased in recent years, the lack of standardisation across tools still makes it difficult to assess the reliability and comparability of MR studies. Developing more complete and standardised evaluation frameworks and properly testing these tools in practice are important next steps to improve the overall quality of MR research.

Key Messages

• Mendelian randomisation (MR) studies are increasingly applied, but there is still no standard approach for assessing their quality and risk of bias.

• Our updated systematic review identified 15 new articles since the review by Spiga et al., which included studies published up to July 2021, bringing the total to 29 tools, with 19 providing structured tools for evaluation.

• Newer tools cover a broader range of methodological domains beyond the three core instrumental variable assumptions, including genetic instrument selection, population stratification, sensitivity analyses, and dataset considerations.

• There are still large differences between tools in structure, scoring methods, and bias domains covered, and some important areas such as linkage disequilibrium and missing data remain under-assessed.