The virtues of a deliberately mis-specified disease model in demonstrating a gene-environment interaction
Bibliographic record
Abstract
OBJECTIVES: This study seeks to assess the impact of measurement errors in cumulative exposure on estimates of a gene-environment interaction in a nested case-control study in occupational epidemiology. In the approach considered here, exposure intensity is assessed at the group level and the exposure duration individually (both with error). Genetic susceptibility is assumed to be known exactly. Differences in "gene" are assumed to affect disease risk only in exposed subjects. METHODS: Three data analysis strategies were considered: one using a correctly specified disease model (exposure and exposure-gene interaction), and two using mis-specified disease models, one with "gene" as the only risk factor ("gene-only" model) and the other with main effects of both gene and exposure along with their interaction ("full" model). RESULTS: In simulations, estimates of the gene-environment interaction based on the correctly specified disease model were greatly attenuated and power was diminished appreciably even when errors in exposure were modest. Significant associations were detected more frequently in the gene-only model when errors in exposure were large. When the "full" mis-specified model was fitted to the simulated data, it yielded erratic estimates. This is illustrated in an analysis of the interaction of cumulative exposure to organophosphate pesticides and paraoxonase gene on the risk of chronic neuropsychological effects among farmers who dip sheep. CONCLUSION: If "gene" contributes to disease risk only in the presence of exposure, the existence of the gene-environment interaction can be efficiently inferred from a deliberately mis-specified "gene-only" disease model in nested case-control 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 | 0.035 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".