Use of causal claims in observational studies: a research on research study

Objective: To evaluate the consistency of causal statements in the abstracts of observational studies published in The BMJ. Design: Research on research study. Data source: All cohort or longitudinal studies describing an exposure-outcome relationship published in The BMJ during 2018. We also had access to the submitted papers and reviewer reports. Main outcome measures: Proportion of published research papers with 9inconsistent9 use of causal language in the abstract. Papers where language was consistently causal or non-causal were classified as 9consistently causal9 or 9consistently not causal9, respectively; those where causality may be inferred were classified as 9suggests causal9. For the 9inconsistent9 papers, we then compared the published and submitted version. Results: Of 151 published research papers, 60 described eligible studies. Of these 60, we classified the causal language used as 9consistently causal9 (13%), 9suggests causal9 (35%), 9inconsistent9 (20%) and 9consistently not causal9(32%). The majority of the 9Inconsistent9 papers (92%) were already inconsistent on submission. The inconsistencies found in both submitted and published versions was mainly due to mismatches between objectives and conclusions. One section might be carefully phrased in terms of association while the other presented causal language. When identifying only an association, some authors jumped to recommending acting on the findings as if motivated by the evidence presented. Conclusion: Further guidance is necessary for authors on what constitutes a causal statement and how to justify or discuss assumptions involved. Based on screening these abstracts, we provide a list of expressions beyond the obvious 9cause9 word which may inspire a useful more comprehensive compendium on causal language.