Doubly weighted estimating equations and weighted multiple imputation for causal inference with an incomplete subgroup variable
Bibliographic record
Abstract
Health research often aims to investigate whether the effect of an exposure variable is common across different subgroups of individuals, but sometimes the variable defining subgroups is not recorded in all individuals. We propose and evaluate two methods for estimation of the marginal causal effect of an exposure variable within subgroups in the observational setting where the subgroup variable is incompletely observed. The first approach involves doubly weighted estimating functions with one weight based on a propensity score for exposure and a second weight addressing the selection bias when analyses are restricted to individuals with complete data. The second approach uses the inverse probability of exposure weights in conjunction with multiple imputation for the incomplete subgroup variable. The resulting estimators are consistent when the auxiliary models are correctly specified; we assess the finite sample performance via simulation. An illustrative analysis is provided involving patients with psoriatic arthritis treated with biologic therapy where interest lies in the effect of therapy according to the presence or absence of the human leukocyte antigen marker HLA-B27 which is incompletely observed.
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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 | 0.037 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".