The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Randomization, Statistics, and Causal Inference
Abstract
This paper reviews the role of statistics in causal inference. Special attention is given to the need for randomization to justify causal inferences from conventional statistics, and the need for random sampling to justify descriptive inferences. In most epidemiologic studies, randomization and random sampling play little or no role in the assembly of study cohorts. I therefore conclude that probabilistic interpretations of conventional statistics are rarely justified, and that such interpretations may encourage misinterpretation of nonrandomized studies. Possible remedies for this problem include deemphasizing inferential statistics in favor of data descriptors, and adopting statistical techniques based on more realistic probability models than those in common use.
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“The latter do not take into account other uncertainties that arise from a study's design, implementation, and methods of measurement [ 209 ].”
From Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration (2007) · cites this paper as [209]
Citations
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Modeling and variable selection in epidemiologic analysis
Greenland, S. (1989) · American Journal of Public Health · doi:10.2105/ajph.79.3.340 · Cited by 2,208
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Moher, D., Hopewell, S., Schulz, K. F., Montori, V., Gotzsche, P. C., Devereaux, P. J., Elbourne, D., Egger, M., & Altman, D. G. (2010) · BMJ · doi:10.1136/bmj.c869 · Cited by 8,883
Vandenbroucke, J. P., von Elm, E., Altman, D. G., Gøtzsche, P. C., Mulrow, C. D., Pocock, S. J., Poole, C., Schlesselman, J. J., Egger, M., & Initiative, F. T. S. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040297 · Cited by 8,613
“The latter do not take into account other uncertainties that arise from a study’s design, implementation, and methods of measurement [209].”
Sensitivity analysis in observational research: Introducing the E-value
VanderWeele, T. J., & Ding, P. (2017) · Annals of Internal Medicine · doi:10.7326/m16-2607 · Cited by 6,350
“, that some third factor related to both the treatment and the outcome might explain their association, with no true causal effect(12-22).”
BackgroundSPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials
Chan, A.-W., Tetzlaff, J. M., Gotzsche, P. C., Altman, D. G., Mann, H., Berlin, J. A., Dickersin, K., Hrobjartsson, A., Schulz, K. F., Parulekar, W. R., et al. (2013) · BMJ · doi:10.1136/bmj.e7586 · Cited by 6,251
The Revised CONSORT Statement for Reporting Randomized Trials: Explanation and Elaboration
Altman, D. G., Schulz, K. F., Moher, D., Egger, M., Davidoff, F., Elbourne, D., Gøtzsche, P. C., Lang, T., & for the CONSORT Group (2001) · Annals of Internal Medicine · doi:10.7326/0003-4819-134-8-200104170-00012 · Cited by 3,835
Metadata
- Authors
- Greenland, S.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Greenland, Sander
- ORCID
- Greenland, Sander
- Venue
- Epidemiology
- Published
- 1990-11
- Volume
- 1
- Issue
- 6
- Pages
- 421-429
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W2038826970
- Cited by
- 385
- References
- 0
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 12 · 2023: 11 · 2022: 16 · 2021: 18 · 2020: 21 · 2019: 7 · 2018: 7 · 2017: 14 · 2016: 13 · 2015: 11 · 2014: 9 · 2013: 10 · 2012: 13
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Keywords
- causal inference, randomization, random sampling, statistical inference, data descriptors, probability models, epidemiological study design
- MeSH terms
- Bayes Theorem, Data Interpretation, Statistical, Epidemiologic Methods, Humans, Causality, Confounding Factors, Epidemiologic, Randomized Controlled Trials as Topic
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