Inverse Probability of Treatment Weighting Using the Propensity Score With Competing Risks in Survival Analysis
Bibliographic record
Abstract
Inverse probability of treatment weighting (IPTW) using the propensity score allows estimation of the effect of treatment in observational studies. We had three objectives: first, to describe methods for using IPTW to estimate the effects of treatments in settings with competing risks; second, to illustrate the application of these methods using empirical analyses; and third, to conduct Monte Carlo simulations to evaluate the relative performance of three methods for estimating time-specific risk differences and time-specific relative risks in settings with competing risks. In doing so, we provide guidance to applied biostatisticians and clinical investigators on the use of IPTW in settings with competing risks. We examined three estimators of time-specific risk differences and relative risks: the weighted Aalen-Johansen estimator, an estimator that combines IPTW with inverse probability of censoring weights (IPTW-IPCWs), and a double-robust augmented IPTW estimator combined with IPCW (AIPTW-IPCW). The design of our simulations reflected clinically realistic scenarios. Our simulations found that all three estimators tended to result in unbiased estimations of time-specific risk differences and time-specific relative risks. However, the weighted Aalen-Johansen estimator and the AIPTW-IPCW estimator tended to result in estimates with greater precision compared to the IPTW-IPCW estimator. In our empirical analyses, we illustrated the application of these methods by estimating the effect of statin prescribing on the risk of subsequent cardiovascular death in patients discharged from the hospital with a diagnosis of acute myocardial infarction.
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 | 0.092 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".