Risk Factors, Confounding, and the Illusion of Statistical Control
Abstract
When experimental designs are premature, impractical, or impossible, researchers must rely on statistical methods to adjust for potentially confounding effects. Such procedures, however, are quite fallible. We examine several errors that often follow the use of statistical adjustment. The first is inferring a factor is causal because it predicts an outcome even after "statistical control" for other factors. This inference is fallacious when (as usual) such control involves removing the linear contribution of imperfectly measured variables, or when some confounders remain unmeasured. The converse fallacy is inferring a factor is not causally important because its association with the outcome is attenuated or eliminated by the inclusion of covariates in the adjustment process. This attenuation may only reflect that the covariates treated as confounders are actually mediators (intermediates) and critical to the causal chain from the study factor to the study outcome. Other problems arise due to mismeasurement of the study factor or outcome, or because these study variables are only proxies for underlying constructs. Statistical adjustment serves a useful function, but it cannot transform observational studies into natural experiments, and involves far more subjective judgment than many users realize.
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The validity of results often depends crucially on complete knowledge of important confounders, their precise measurement, and appropriate specification in the statistical model.
“Thus, great care should be exercised when interpreting adjusted results, as the validity of results often depends crucially on complete knowledge of important confounders, their precise measurement, and appropriate specification in the statistical model (see also item 20) [ 157 , 158 ].”
From Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration (2007) · cites this paper as [157]
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- Authors
- Christenfeld, N. J. S., Sloan, R. P., Carroll, D., & Greenland, S.
- Affiliations
- University of California San Diego (US)
- University of California, Los Angeles (US)
- Corresponding author
- Christenfeld, Nicholas J. S.
- Venue
- Psychosomatic Medicine
- Published
- 2004-11
- Volume
- 66
- Issue
- 6
- Pages
- 868-875
- ISSN
- 0033-3174 (ISSN-L)
- OpenAlex
- W2065056866
- Cited by
- 260
- References
- 0
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 8 · 2023: 5 · 2022: 11 · 2021: 21 · 2020: 15 · 2019: 17 · 2018: 7 · 2017: 4 · 2016: 13 · 2015: 8 · 2014: 14 · 2013: 14 · 2012: 28
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychosomatic Medicine (publishedVersion)
- PubMed (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- confounding, statistical adjustment, causal inference, risk factors, measurement error, mediators, covariate adjustment, observational studies, natural experiment
- MeSH terms
- Data Interpretation, Statistical, False Positive Reactions, Humans, Research Design, Risk Factors, Reproducibility of Results, Health Behavior, Bias, Confounding Factors, Epidemiologic, Randomized Controlled Trials as Topic, Outcome Assessment, Health Care, Observation
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