The effect of assigning different index dates for control exposure measurement on odds ratio estimates
Bibliographic record
Abstract
In case-control studies it is reasonable to consider the exposure history of a case prior to disease onset. For the controls, it is necessary to define comparable periods of exposure opportunity. Motivated by data from a case-control study of the environmental risk factors for Multiple Sclerosis we propose a control-to-case matching algorithm that assigns pseudo ages at onset, index ages, to the controls. Based on a simulation study, we conclude that our index age algorithms yields a greater power than the default method of assigning a control's current age as their index age, especially for moderate effects. Furthermore, we present theoretical results that show that for binary and ordered categorical exposure variables using an inappropriate index age assignment method can obscure or even mask a true effect. The effect of the choice of index age assignment method on the inference on the odds ratio is highly data dependent. In contrast to the results of our simulation study, our analysis of the data from the motivating case-control study resulted in odds ratio and variance estimates that were very similar regardless of the choice of the method of assigning index ages.
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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 | 0.159 | 0.482 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".