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The 'Table 2 Fallacy' is a term coined by Daniel Westreich and Sander Greenland in 2013.[1] It is a concept in causal inference.
Table 2 Fallacy: The misconception that the associations between confounders and the outcome can be interpreted as valid estimates of causal associations between each confounder and the outcome.
Maarten van Smeden, A Very Short List of Common Pitfalls in Research Design, Data Analysis, and Reporting, https://journals.stfm.org/primer/2022/van-smeden-2022-0059/
In scientific papers reporting observational studies, people often report both crude and adjusted associations between variables included in a regression model and the outcome of interest in Table 2. If the purpose of the analysis is causal inference, usually, the variables one would choose to adjust for will differ for each exposure - outcome pairing. The Table 2 Fallacy occurs when people seek to give a causal interpretation to the other parameters estimated using a multivariable regression model that was only designed to explore a single exposure - outcome association.
Table 2 Fallacy is a common error made in reporting epidemiological results in a wide range of subject areas.[2][3][4][5][6]
A high profile example was a paper exploring associations between demographic and health characteristics and death from COVID-19,[7] which was used by the French government to define which groups of workers were deemed at risk.[8] A number of letters to the editor argued that the paper wrongly implied that causal interpretations should be given to multiple parameters taken from a single multivariable regression model.[9][10] This claim was contested by the study authors, who argued that their paper did not make causal claims.[11]
References
[edit]- ^ "The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients".
- ^ Bandoli, G.; Palmsten, K.; Chambers, C. D.; Jelliffe-Pawlowski, L. L.; Baer, R. J.; Thompson, C. A. (2018). "Revisiting the Table 2 Fallacy: A Motivating Example Examining Preeclampsia and Preterm Birth". Paediatric and Perinatal Epidemiology. 32 (4): 390–397. doi:10.1111/ppe.12474. PMC 6103824. PMID 29782045.
- ^ Green, M. J.; Popham, F. (2019). "Interpreting mutual adjustment for multiple indicators of socioeconomic position without committing mutual adjustment fallacies". BMC Public Health. 19: 10. doi:10.1186/s12889-018-6364-y. PMC 6319005. PMID 30606167.
- ^ Akinkugbe, Aderonke A.; Simon, Alyssa M.; Brody, Erica R. (2021). "A scoping review of Table 2 fallacy in the oral health literature". Community Dentistry and Oral Epidemiology. 49 (2): 103–109. doi:10.1111/cdoe.12617. PMID 33368566.
- ^ Ponkilainen, V. T.; Uimonen, M.; Raittio, L.; Kuitunen, I.; Eskelinen, A.; Reito, A. (July 2021). "Multivariable models in orthopaedic research: a methodological review of covariate selection and causal relationships". Osteoarthritis and Cartilage. 29 (7): 939–945. doi:10.1016/j.joca.2021.03.020.
- ^ Kezios, K. L. (2021). "Is the Way Forward to Step Back? Documenting the Frequency With Which Study Goals Are Misaligned With Study Methods and Interpretations in the Epidemiologic Literature". Epidemiologic Reviews. 43 (1): 4–18. doi:10.1093/epirev/mxab008. PMC 9005115. PMID 34535799.
- ^ Williamson, Elizabeth J.; Walker, Alex J.; Bhaskaran, Krishnan; Bacon, Seb; Bates, Chris; Morton, Caroline E.; Curtis, Helen J.; Mehrkar, Amir; Evans, David; Inglesby, Peter; Cockburn, Jonathan; McDonald, Helen I.; MacKenna, Brian; Tomlinson, Laurie; Douglas, Ian J.; Rentsch, Christopher T.; Mathur, Rohini; Wong, Angel Y. S.; Grieve, Richard; Harrison, David; Forbes, Harriet; Schultze, Anna; Croker, Richard; Parry, John; Hester, Frank; Harper, Sam; Perera, Rafael; Evans, Stephen J. W.; Smeeth, Liam; Goldacre, Ben (2020). "Factors associated with COVID-19-related death using OpenSAFELY". Nature. 584 (7821): 430–436. doi:10.1038/s41586-020-2521-4. PMC 7611074. PMID 32640463.
- ^ "Tweet by Olivier Berruyer".
- ^ Westreich, D.; Edwards, J. K.; Van Smeden, M. (2021). "Comment on Williamson et al. (OpenSAFELY): The Table 2 Fallacy in a Study of COVID-19 Mortality Risk Factors". Epidemiology (Cambridge, Mass.). 32 (1): e1–e2. doi:10.1097/EDE.0000000000001259. PMID 33065610.
- ^ Tennant PWG; Murray, E. J. (2021). "The Quest for Timely Insights into COVID-19 Should not Come at the Cost of Scientific Rigor". Epidemiology (Cambridge, Mass.). 32 (1): e2. doi:10.1097/EDE.0000000000001258. PMID 33065609.
- ^ Williamson, E.; Bhaskaran, K.; Walker, A.; Smeeth, L.; Goldacre, B. (2021). "The Authors Respond". Epidemiology (Cambridge, Mass.). 32 (1): e2–e3. doi:10.1097/EDE.0000000000001260. PMID 33105269.