Falsified Data in Meta-Analyses - a novel study finds Trojan horse inside the gold standard

Clinical trials under the U.S. Food and Drug Administration’s (FDA) purview have been shown to suffer from falsified data. While the FDA warns researchers when falsified data are discovered, these data still make their way into medical literature.

In a study published in JAMA Internal Medicine , a team of researchers at FIU’s Robert Stempel College of Public Health and Social Work, led by Dr. Craig A. Garmendia, has found that almost half of all meta-analyses had conclusions altered by falsified data publications. This resulted in nearly 1/3 of all analyses having considerable changes in outcomes.

"The desire to include all available data in a meta-analysis to obtain the ‘best estimate’ of effect size may result in the inclusion of falsified data,” said Garmendia. “This in turn may provide biased results that compromise future research, policy decisions, and even patient care.”

This study analyzed the effect of publications with falsified data on the results of meta-analyses, which can be affected by the inclusion of publications with falsified data. Robust sensitivity analyses and prevention of publication of falsified data, affecting not only the original publication but also any subsequent meta-analyses, is needed to avoid compromising future research, policy decisions, and patient care.

This study found that 45.5% of all meta-analysis publications had conclusions altered by publications with falsified data and 32.3% of all the analyses having a considerable change in the outcome. Full meta-analyses were more robust against the effects of publications with falsified data. For odds ratios not statistically affected, the estimates generally moved towards the null when more than one publication remained.

The sensitivity analysis results showed meta-analyses can suffer from the inclusion of publications with falsified data, so much so that conclusions may be altered. This study should add impetus for robust sensitivity analyses and stronger protections against falsified data (e.g., x). Falsified data can affect not only the original publication, but also any subsequent meta-analyses and any resulting clinical or policy changes resulting from the findings of these studies.