{"doi":"10.1093/ije/dyaa094","title":"Commentary: Developing best-practice guidelines for the reporting of E-values","abstract":"We would like to thank Blum et al.1 for their interesting analysis of the current reporting practices around the use of the E-value to evaluate sensitivity to unmeasured confounding.2,3 As with nearly any quantitative tool, the E-value is potentially subject to misuse,4,5 examples of which are indeed documented in their paper. This arguably also points to the need for the development of best-practice guidelines for the reporting and interpretation of E-values. We will here offer some reflections on potential reporting guidelines. Blum et al.1 sampled a set of control papers from the same journals as those that reported E-values. Of these 69 papers, 52 (75.3%) apparently had no discussion whatsoever of unmeasured confounding. Unmeasured confounding is a major threat in most observational studies. That this threat is left both undiscussed and unquantified so frequently is troubling. Some form of sensitivity analysis or critical assessment of potential uncontrolled confounding is needed to address this problem. The E-value was developed as a particularly straightforward approach to do so,3,4,6 but there are of course other tools available.7,8 However, some approach should be employed. As noted in our paper3 and by Blum et al.1, the E-value is not context-free. The E-value needs to be evaluated in light of the measured confounders, the outcome, the exposure and the potentially known unmeasured confounders.2–4,9 Whenever possible, it would be good to report specific variables that are thought to be potential unmeasured confounders. Blum et al.1 report that, of the 87 articles in their sample, 34 (39%) named specific variables that could be confounders and were unaccounted for. Such reporting should be improved. There are settings in which all known risk factors for an outcome are controlled for and, in such circumstances, it is not possible to specifically name a potential unmeasured confounder, but it is unlikely that this constitutes the remaining 61% of cases. Even in such settings wherein no specific unmeasured confounder can be named, it can be worthwhile calculating E-values or performing some other sensitivity analysis, as unknown unmeasured confounders can still be a threat. However, as a general principle, it would be good if all papers either stated that control was made for all known confounders or alternatively discussed which important unmeasured confounders might still have biased the analysis. Blum et al.1 rightly emphasize the need to interpret the magnitude of the required confounding associations. In our papers,2–4 we have not provided ‘cut-offs’ for what were large or small E-values. That will be relative to the outcome and exposure under consideration.2–4,9 It will also be relative to what measured confounders have been adjusted for.2–4,9 If adjustment has been made for numerous measured confounders related to the unmeasured variable, then the residual confounding associations are likely to be small. In contrast, if there are multiple unmeasured confounding variables, then it is possible for the residual confounding associations to be very large and, in such cases, not even a large E-value would provide much evidence for causation.2–4,9 However, if there are several (e.g., three or four) distinct important known unmeasured confounders, then a reasonable effect estimate likely cannot be obtained to begin with (these would not be the right data with which to attempt to address the research question). The E-value approach, and sensitivity analysis more generally, will be most helpful when there is a single known unmeasured confounder, or when adjustment has been made for all known measured confounders but, of course, with the possibility still of an unknown unmeasured confounder. With a single known or unknown unmeasured confounder, it is still important to have some sense as to how large the confounding associations may be. Previous studies that have measured the variable in question (if there is a known unmeasure","journal":"International Journal of Epidemiology","year":2020,"id":118330,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":110,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9594,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":32607,"name":"Maya B. Mathur","orcid":"0000-0001-6698-2607","position":1,"is_corresponding":false},{"id":231697,"name":"Tyler J. VanderWeele","orcid":"0000-0002-6112-0239","position":0,"is_corresponding":true}],"reference_count":15,"raw_metadata":null,"created_at":"2026-07-18T23:13:58.531532Z","pmid":"32743656","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}