Critical appraisal of the application of propensity score methods in the urology literature
Bibliographic record
Abstract
OBJECTIVES: To determine whether studies that used propensity score (PS) methods in the urology literature provide sufficient detail to allow scientific reproducibility and whether appropriate statistical tests were used to obtain valid measures of effect. MATERIALS AND METHODS: We searched OVID Medline and the Science Citation Index from inception to November 2016 to identify studies that used PS methods in five general urology journals. From each included article, we extracted pertinent information related to the PS methodology, such as estimation of the PS, whether balance diagnostics were performed, and the statistical analysis performed. RESULTS: We identified 114 articles for inclusion. Matching on the PS was the most common method used (62 studies, 54.4%). Of all studies, 103 (90.4%) described which covariates were used to estimate the PS; however, only 24 provided justification for the selected covariates. Although the majority of studies (70.2%) performed some sort of diagnostic evaluation to assess balance, few studies (24.6%) used appropriate methods for balance assessment. Only four (6.4%) studies that used PS matching provided sufficient detail to replicate the matching strategy. Finally, the majority (77.4%) of studies that used PS matching explicitly used inappropriate statistical methods to estimate the effect of an exposure on an outcome. CONCLUSIONS: In the urology literature PS methods were poorly described and implemented. We provide recommendations for improvement to allow scientific reproducibility and obtain valid measures of effect from their use.
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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 | 0.518 | 0.863 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.053 | 0.024 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".