Doubly weighted mean score estimating functions with a partially observed effect modifier
Bibliographic record
Abstract
Effect modification plays a central role in stratified medicine, of which the goal is often to find biomarker profiles that identify individuals who benefit from a treatment of interest. We consider the problem of causal inference regarding the effect modifying role of a biomarker, which is only available for some individuals in an observational study. We develop inverse probability weighted mean score estimating functions with one weight to account for confounding and a second weight for the missing data process. An iterative approach is described for solving the equations in the spirit of the expectation-maximization algorithm, and large sample properties of the resulting estimator are developed. Simulation studies are conducted to compare the proposed method with a doubly weighted complete case analysis and a propensity score weighted multiple imputation approach. An application to a study of the effect of a biologic therapy on inflammation in a rheumatology cohort is given for illustration.
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How this classification was reachedexpand
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 | 0.006 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".