Confounders and intermediaries in case–control study designs: a strategy for distinguishing between the two when measured using the same variable
Bibliographic record
Abstract
PURPOSE: An intermediary falls within the exposure-outcome pathway and is distinct from a confounder. In case-control studies, it may be difficult to discern between the two when both are measured by the same variable. Using data from a study on the effects of antipsychotic initiation on risk of death among older adults, where hospital use is both a confounder and intermediary, we illustrate the bias introduced when this distinction is overlooked and propose a modified exposure classification strategy to mitigate this. METHODS: We identified 5391 cases and 25,937 controls. Three analyses were completed: traditional analytic adjustment including hospital use (full), traditional analytic adjustment excluding hospital use (reduced) and exposure classification incorporating hospital use prior to antipsychotic initiation (extended). RESULTS: The unadjusted odds ratio (OR) was 2.8 (95% confidence interval (CI) 2.1-3.8). Full and reduced analytic adjustment resulted in ORs of 0.8 (95% CI 0.6-1.2) and 1.4 (95% CI 1.0-1.9), respectively. The extended exposure classification strategy produced an OR of 1.4 (95% CI 0.9-2.1) among those without hospital use prior to antipsychotic initiation. CONCLUSIONS: Full analytic adjustment resulted in a biased estimate of effect. The extended exposure analysis differentiated between hospital use that occurred prior (confounder) and subsequent (intermediary) to antipsychotic initiation. This strategy may overcome the limitations of analytic adjustment alone.
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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 | 0.466 | 0.607 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".