Comparisons of Propensity Score Methods for Time to Event Outcomes: Evaluation through Simulations and Oral Squamous Cell Carcinoma Case Study
Bibliographic record
Abstract
Introduction & Objective: In observational studies, it is recommended to use propensity score (PS) methods or covariate adjustment for confounding effect adjustment. However, few guidelines are available regarding the choice of PS approaches or covariate adjustment for the best performance in a particular data. In this study, we compared different PS methods and conventional covariate adjustment to investigate the treatment effect for the overall population on time-to-event outcomes. Methods: In the Monte Carlo simulations, we compared the hazard ratio (HR) and precision estimated using covariate adjustment and eight different PS approaches, including matching, stratification, and inverse probability of treatment weighting (IPTW). In the Oral Squamous-Cell Carcinoma Cancer case study, we applied the aforementioned PS approaches to compare the effect of receiving post-operative radiation therapy (PORT) and having engraftable tumors on different time-to-event clinical outcomes. Results: In the simulations, both IPTW and covariate adjustment produced unbiased HR estimates with small uncertainty. In the case study, covariate adjustment showed that patients with engraftable tumors were twice as likely to have local/regional recurrence (HR 1.98 [1.23, 3.18], p-value<0.005) and any recurrence or death (HR 2.02 [1.38, 2.96], p-value<0.001); patients received PORT were twice as likely to develop either local, regional, or distance recurrence (HR 2.12 [1.32, 3.41], p-value<0.005). Results produced by IPTW were consistent with covariate adjustment method (within ± 0.1 differences). Conclusion: Covariate adjustment and the IPTW method performed well across simulations and the case study. In practice, care should be taken to select the most suitable method when estimating the treatment, exposure or intervention effect on time-to-event outcomes.
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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 | 0.066 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".