Researchers develop tool that ‘audits’ the results of studies that examine interplay between variables. Using this approach, researchers identify inconsistent findings in one-third of previous association studies of gut microbiome and disease. The software can help researchers check the reliability of their own findings before submitting them for publication. Association studies are critical in paving the way to further research into disease causes and mechanisms of dysfunction.

It depends.

The same research question can yield vastly different answers depending on how a study is designed, which variables are measured, and how results are analyzed. Because of the hodgepodge of approaches used to decipher the interplay between variables, association studies — those that explore how one thing affects another — are notoriously prone to error or «bias.» Finding a false link where none exists or missing one if it does can thwart the pursuit of critical scientific questions and solutions, lead researchers down the wrong path and generate contradictory results that confuse peer scientists and the public alike.

To help remedy such problems, a team of computational scientists from Harvard Medical School has developed an auditing tool called vibration of effects (VoE). The tool, first described in PLoS Biology in September 2021, has now been deployed to analyze reported links between various gut microbes and six diseases in 15 previously published studies comprising samples from 2,434 patients with colon cancer, type 1 diabetes, type 2 diabetes, cardiovascular disease, inflammatory bowel disease (IBD) and cirrhosis of the liver.

The newly published research is the final installment in a three-paper series and represents the culmination of the team’s two-year journey undertaken at the start of the COVID-19 pandemic and conducted with collaborators working remotely across the country.

The results of the latest study, published March 2 in PLoS Biology, reveals that a full one-third of 581 reported microbe-disease associations were inconsistent, with outcomes changing depending on how the design was tweaked and which other variables were included in the analysis.

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Materials provided by Harvard Medical School. Original written by Ekaterina Pesheva. Note: Content may be edited for style and length.