Variance estimation when using propensity‐score matching with replacement with survival or time‐to‐event outcomes
Bibliographic record
Abstract
Propensity-score matching is a popular analytic method to estimate the effects of treatments when using observational data. Matching on the propensity score typically requires a pool of potential controls that is larger than the number of treated or exposed subjects. The most common approach to matching on the propensity score is matching without replacement, in which each control subject is matched to at most one treated subject. Failure to find a matched control for each treated subject can lead to "bias due to incomplete matching." To avoid this bias, it is important to identify a matched control subject for each treated subject. An alternative to matching without replacement is matching with replacement, in which control subjects are allowed to be matched to multiple treated subjects. A limitation to the use of matching with replacement is that variance estimation must account for both the matched nature of the sample and for some control subjects being included in multiple matched sets. While a variance estimator has been proposed for when outcomes are continuous, no such estimator has been proposed for use with time-to-event outcomes, which are common in medical and epidemiological research. We propose a variance estimator for the hazard ratio when matching with replacement. We conducted a series of Monte Carlo simulations to examine the performance of this estimator. We illustrate the utility of matching with replacement to estimate the effect of smoking cessation counseling on survival in smokers discharged from hospital with a heart attack.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
| grok | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
| opus | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
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 | 0.048 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".