Statistical pitfalls of multiple exposures in causal observational studies and tools to address them
Bibliographic record
Abstract
ABSTRACT The Table 2 Fallacy is an interpretation error commonly encountered in medical literature. This fallacy occurs when coefficient estimates in multivariable regression models, apart from that of the primary exposure, are interpreted as total effects on the outcome. Causal diagrams can be used to identify sets of covariates that, when adjusted for, allow for unbiased estimation and correct interpretation of multiple total effects of interest. However, proper investigation of multiple total effects requires fitting several regression models and conducting multiple inferences. As the number of inferences increases, so does the rate of a false positive finding, a phenomenon known as multiplicity. While multiple comparison procedures are recognized as a critical consideration of randomized controlled trials, opinion remains divided on their use within observational studies. This commentary highlights how multiplicity may arise alongside the Table 2 Fallacy, and how causal diagrams can be used in conjunction with multiple comparison procedures to simultaneously avoid this fallacy, control the risk of spurious findings, and further align the best practices of experimental and observational studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.429 | 0.743 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".