Estimation of high‐dimensional propensity scores with multiple exposure levels
Bibliographic record
Abstract
PURPOSE: Little information is available on the performance of high-dimensional propensity scores (HDPS) in settings with more than two exposure levels. Our objective was to adapt the HDPS algorithm to allow for the inclusion of multilevel treatments and compare estimates obtained via this approach with those obtained via pairwise comparisons in a case study using real-world data. METHODS: We conducted a retrospective cohort study of cardiovascular events associated with three smoking cessation drugs (varenicline, bupropion, nicotine replacement therapy [NRT]) using the Clinical Practice Research Datalink. We applied the binary HDPS algorithm adjusted for pre-specified and empirically-selected covariates to cohorts formed by each treatment pair. We then constructed multinomial HDPS models on a cohort of new users of any of the three drugs, adjusting for predefined covariates and different combinations of empirically-selected covariates. After trimming the area of non-overlap of the HDPS distributions, the effects of the study drugs on cardiovascular events were estimated with the Cox proportional hazards models adjusted for propensity score category. RESULTS: = 0.76). Trimming rates were similar between the two approaches. CONCLUSIONS: The extension of HDPS to multilevel exposures is a valid and practical approach to confounder control that may be useful when comparing different classes of drugs prescribed for the same indication or different molecules within a given drug class.
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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 | 0.049 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".