Marginal Versus Conditional Odds Ratios When Updating Risk Prediction Models
Why this work is in the frame
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Bibliographic record
Abstract
Risk prediction models often need to be updated when applied to new settings. A simple updating method involves fixed odds ratio transformation of predicted risks to adjust the model for outcome prevalence in the new setting. When a sample from the target population is available, the gold standard is to use a logistic regression model to estimate this odds ratio. A simpler method has been proposed that calculates this odds ratio from the prevalence estimates in the original and new samples. We show that the marginal odds ratio estimated in this way is generally closer to one than the correct (conditional) odds ratio; thus, the simpler method should be avoided when individual-level data are available. When such data are not available, we suggest an approximate method for recovering the conditional odds ratio from the variance of predicted risks in the development sample. Brief simulations and examples show that this approach reduces undercorrection, often substantially.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it