Using Propensity Score Matching in Clinical Investigations: A Discussion and Illustration
Bibliographic record
Abstract
Propensity score matching is a useful tool to analyze observational data in clinical investigations, but it is often executed in an overly simplistic manner, failing to use the data in the best possible way. This review discusses current best practices in propensity score matching, outlining the method’s essential steps, including appropriate post-matching balance assessments and sensitivity analyses. These steps are summarized as eight key traits of a propensity matched study. Further, this review illustrates these traits through a case study examining the impact of access site in percutaneous coronary intervention (PCI) procedures on bleeding complications. Through propensity score matching, we find that bleeding occurs significantly less often with radial access procedures, though many other outcomes show no significant difference by access site, a finding that mirrors the results of randomized controlled trials. Lack of attention to methodological principles can result in results that are not biologically plausible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.101 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".