Tuesday, 5 April 2022

The sorry state of vaping research

Dozens of new studies about vaping are published every week and most of them are absolutely dismal. They start from a false premise, use biased terminology, treat 'EVALI' as if it was caused by conventional vaping, fail to distinguish the effect of vaping from the effect of prior smoking, treat correlation as causation, and employ poor methodologies to come up with a desired anti-vaping conclusion.

A new study titled 'Analysis of common methodological flaws in the highest cited e-cigarette epidemiology research' shows just what a sorry state the field is in, particularly the garbage that gets the most citations and is most attractive to journalists. 

Well done to the authors for doing this deep dive. Here is their conclusion:

Our critical appraisal reveals common, preventable flaws, the identification of which may provide guidance to researchers, reviewers, scientific editor, journalists, and policy makers. One striking result of the review is that a large portion of the high-ranking papers came out of US-dominated research institutions whose funders are unsupportive of a tobacco harm reduction agenda.

However, this does not mean there is a trove of good research out there that answers the big questions, but merely did not make the popularity cut. There is not. Notably, papers discussing the effect of vaping on smoking initiation shared common flaws. By contrast, papers addressing the effect of vaping on smoking cessation or reduction demonstrated a broader variety of flaws, yet common themes emerged. Our analysis of common flaws and limitations may guide future researchers to conduct more robust studies and, concomitantly, produce more reliable literature. There are countless sources of good building-block information that can be pieced together to provide knowledge. To provide useful information, research questions should be precise, contingent, nuanced and focused on quantifications that are motivated by externally defined questions. Such research necessitates proactive design, rather than utilizing already existing, but not fit-for-purpose, datasets.

Do read it all. No paywall.

No comments: