ArticleBiostatistics (Oxford, England)2021
Bias due to Berkson error: issues when using predicted values in place of observed covariates.
Article in Biostatistics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Heatwaves, Cold Spells, and Renal Disease Mortality in Japan: Vulnerable Populations and Temporal Trends (1979-2019).Environment & health (Washington, D.C.) · 2026Article
- Determination of Offset Values in Binary Regression Models to Adjust for Misclassification Errors.International journal of environmental research and public health · 2026Article
- Causal Covariate Selection for the Regression Calibration Method for Exposure Measurement Error Bias Correction.Statistics in medicine · 2026Article
- Analyses using multiple imputation need to consider missing data in auxiliary variables.American journal of epidemiology · 2025Article
- A WARNING ABOUT USING PREDICTED VALUES TO ESTIMATE DESCRIPTIVE MEASURES.American journal of epidemiology · 2023Article
- Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality.Journal of agricultural, biological, and environmental statistics · 2023Article
- Mortality risk associated with greenness, air pollution, and physical activity in a representative U.S. cohort.The Science of the total environment · 2022Article
- Dietary fat and fatty acids in relation to risk of colorectal cancer.European journal of nutrition · 2022Article
- The Measurement Error Elephant in the Room: Challenges and Solutions to Measurement Error in Epidemiology.Epidemiologic reviews · 2022Review
- The perils of using predicted values in place of observed covariates: an example of predicted values of body composition and mortality risk.The American journal of clinical nutrition · 2021Article
- Fine Particulate Matter Air Pollution and Mortality Risk Among US Cancer Patients and Survivors.JNCI cancer spectrum · 2021Article
- Ambient temperature and mental health hospitalizations in Bern, Switzerland: A 45-year time-series study.PloS one · 2021Article
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Authors and funding
3 authors.
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Abstract
Studies often want to test for the association between an unmeasured covariate and an outcome. In the absence of a measurement, the study may substitute values generated from a prediction model. Justification for such methods can be found by noting that, with standard assumptions, this is equivalent to fitting a regression model for an outcome variable when at least one covariate is measured with Berkson error. Under this setting, it is known that consistent or nearly consistent inference can be obtained under many linear and nonlinear outcome models. In this article, we focus on the linear regression outcome model and show that this consistency property does not hold when there is unmeasured confounding in the outcome model, in which case the marginal inference based on a covariate measured with Berkson error differs from the same inference based on observed covariates. Since unmeasured confounding is ubiquitous in applications, this severely limits the practical use of such measurements, and, in particular, the substitution of predicted values for observed covariates. These issues are illustrated using data from the National Health and Nutrition Examination Survey to study the joint association of total percent body fat and body mass index with HbA1c. It is shown that using predicted total percent body fat in place of observed percent body fat yields inferences which often differ significantly, in some cases suggesting opposite relationships among covariates.
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