Factors associated with: problems of using exploratory multivariable regression to identify causal risk factors
Bibliographic record
Abstract
Many medical and epidemiological studies use multivariable regression to test whether several independent variables (exposures) are causal determinants of a health outcome. Where mutually adjusted regression coefficients are significant, the exposures are labelled as risk factors for the outcome. We call this study design “factors associated with.” In this article, we argue that this method is flawed due to a lack of reasoning about which variables are treated as confounders, multiple statistical testing, and post hoc interpretation of the results. In some cases, researchers use algorithmic or stepwise approaches to select exposure variables, which further exacerbates these problems. Although the results of factors associated with studies often seem reasonable, these problems mean that the method can also produce implausible results, such as dementia reducing the risk of death in patients admitted to hospital for trauma, 1 diabetes reducing the risk of venous thromboembolism in the general population,2 and lack of food reducing the risk of post-traumatic stress disorder in refugees.3 Many of these studies are published every year, including in well respected journals. We argue that these studies are misleading and contribute to research waste, and the “factors associated with” method should be abandoned.
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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 | 0.066 | 0.333 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".