Use of E-values for addressing confounding in observational studies-an empirical assessment of the literature.

Blum, Manuel; Tan, Yuan Jin; Ioannidis, John P A (2020). Use of E-values for addressing confounding in observational studies-an empirical assessment of the literature. International journal of epidemiology, 49(5), pp. 1482-1494. Oxford University Press 10.1093/ije/dyz261

[img] Text
Blum, Int J Epidemiol 2020.pdf - Published Version
Restricted to registered users only until 14 January 2023.
Available under License Publisher holds Copyright.

Download (504kB) | Request a copy

BACKGROUND

E-values are a recently introduced approach to evaluate confounding in observational studies. We aimed to empirically assess the current use of E-values in published literature.

METHODS

We conducted a systematic literature search for all publications, published up till the end of 2018, which cited at least one of two inceptive E-value papers and presented E-values for original data. For these case publications we identified control publications, matched by journal and issue, where the authors had not calculated E-values.

RESULTS

In total, 87 papers presented 516 E-values. Of the 87 papers, 14 concluded that residual confounding likely threatens at least some of the main conclusions. Seven of these 14 named potential uncontrolled confounders. 19 of 87 papers related E-value magnitudes to expected strengths of field-specific confounders. The median E-value was 1.88, 1.82, and 2.02 for the 43, 348, and 125 E-values where confounding was felt likely to affect the results, unlikely to affect the results, or not commented upon, respectively. The 69 case-control publication pairs dealt with effect sizes of similar magnitude. Of 69 control publications, 52 did not comment on unmeasured confounding and 44/69 case publications concluded that confounding was unlikely to affect study conclusions.

CONCLUSIONS

Few papers using E-values conclude that confounding threatens their results, and their E-values overlap in magnitude with those of papers acknowledging susceptibility to confounding. Facile automation in calculating E-values may compound the already poor handling of confounding. E-values should not be a substitute for careful consideration of potential sources of unmeasured confounding. If used, they should be interpreted in the context of expected confounding in specific fields.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of General Internal Medicine (DAIM) > Clinic of General Internal Medicine

UniBE Contributor:

Blum, Manuel

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0300-5771

Publisher:

Oxford University Press

Language:

English

Submitter:

Tobias Tritschler

Date Deposited:

11 Feb 2020 10:13

Last Modified:

07 Sep 2021 17:17

Publisher DOI:

10.1093/ije/dyz261

PubMed ID:

31930286

Uncontrolled Keywords:

E-value confounding literature review observational study sensitivity analysis

BORIS DOI:

10.7892/boris.139537

URI:

https://boris.unibe.ch/id/eprint/139537

Actions (login required)

Edit item Edit item
Provide Feedback