December 06, 2019 article Open access Use of E-values for addressing confounding in observational studies—an empirical assessment of the literature International Journal of Epidemiology DOI: 10.1093/ije/dyz261 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 3 stored of 104 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Unmeasured confounding in nonrandomized studies: quantitative bias analysis in health technology assessment 2022 Journal of Comparative Effectiveness Research article Mathematics Advanced Causal Inference Techniques Open access 27 citations Commentary: An argument against E-values for assessing the plausibility that an association could be explained away by residual confounding 2020 International Journal of Epidemiology editorial Mathematics Advanced Causal Inference Techniques Open access 29 citations Commentary: Quantifying the unknown unknowns 2020 International Journal of Epidemiology editorial Mathematics Advanced Causal Inference Techniques Open access 3 citations 3 results References 0