Assessing the sensitivity of regression results to unmeasured confounders in observational studies
Open accessAbstract
This paper presents a general approach for assessing the sensitivity of the point and interval estimates of the primary exposure effect in an observational study to the residual confounding effects of unmeasured variable after adjusting for measured covariates. The proposed method assumes that the true exposure effect can be represented in a regression model that includes the exposure indicator as well as the measured and unmeasured confounders. One can use the corresponding reduced model that omits the unmeasured confounder to make statistical inferences about the true exposure effect by specifying the distributions of the unmeasured confounder in the exposed and unexposed groups along with the effects of the unmeasured confounder on the outcome variable. Under certain conditions, there exists a simple algebraic relationship between the true exposure effect in the full model and the apparent exposure effect in the reduced model. One can then estimate the true exposure effect by making a simple adjustment to the point and interval estimates of the apparent exposure effect obtained from standard software or published reports. The proposed method handles both binary response and censored survival time data, accommodates any study design, and allows the unmeasured confounder to be discrete or normally distributed. We describe applications on two major medical studies.
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The study performed an additional sensitivity analysis to determine the robustness of any observed associations to unmeasured confounding using a standard formula-based approach.
“In addition, we performed an additional sensitivity analysis to determine the robustness of any observed associations to unmeasured confounding using a standard formula-based approach (Lin et al., 1998).”
From Maternal hospitalization with infection during pregnancy and risk of autism spectrum disorders (2015) · cites this paper as Lin et al., 1998
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“Sociode-mographic and clinical covariates were included according to general recommendations for observational studies [53] to account for potential confounding or suppression effects and for relevant predictors that have been shown to affect residual fatigue [5, 54].”
Background
- Authors
- Lin, D. Y., Psaty, B. M., & Kronmal, R. A.
- Affiliations
- University of Washington (US)
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1998-09
- Volume
- 54
- Issue
- 3
- Pages
- 948
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2014799253
- Cited by
- 742
- References
- 21
- Citations by year
- 2026: 14 · 2025: 15 · 2024: 23 · 2023: 24 · 2022: 29 · 2021: 30 · 2020: 42 · 2019: 41 · 2018: 47 · 2017: 47 · 2016: 45 · 2015: 45 · 2014: 46 · 2013: 51 · 2012: 39
- FWCI
- 9.38
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Biometrics (publishedVersion)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- National Institutes of Health (5r01ca039929-16)
- National Cancer Institute (CA-39929)
- National Institute of General Medical Sciences (GM47845)
- National Institute of Allergy and Infectious Diseases (AI-29168)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- residual confounding, unmeasured confounding, sensitivity analysis, exposure effect estimation, simplified model, binary outcomes, censored survival data, observational studies
- MeSH terms
- Appetite Depressants, Biometry, Fenfluramine, Cardiac Catheterization, Humans, Hypertension, Pulmonary, Prospective Studies, Regression Analysis, Sensitivity and Specificity, Cohort Studies, Proportional Hazards Models, Survival Analysis, Case-Control Studies
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