The concept of the marginally matched subject in propensity‐score matched analyses
Bibliographic record
Abstract
Propensity-score matching is increasingly being used to reduce the impact of treatment-selection bias when estimating causal treatment effects using observational data. Matching on the propensity score creates sets of treated and untreated subjects who have a similar distribution of baseline covariates. Propensity-score matching frequently relies upon calipers, such that matched treated and untreated subjects must have propensity scores that lie within a specified caliper distance of each other. We define the 'marginally matched' subject as a subject who would be matched using the specified caliper width, but who would not have been matched had calipers with a narrower width been employed. Using patients hospitalized with an acute myocardial infarction (or heart attack) and with exposure to a statin prescription at discharge, we demonstrate that the inclusion of marginally matched subjects can have both a quantitative and qualitative impact upon the estimated treatment effect. Furthermore, marginally matched treated subjects can differ from marginally matched untreated subjects to a substantially greater degree than the differences between non-marginally matched treated and untreated subjects in the propensity-score matched sample. The concept of the marginally matched subject can be used as a sensitivity analysis to examine the impact of the matching method on the estimates of treatment effectiveness.
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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 | 0.118 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| 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".