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| Risk-adjusted Competing Risks Analysis× | Propensity Score Matching× | |
|---|---|---|
| Tieteenala≠ | Epidemiologia | Tutkimuksen tilastomenetelmät |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1999 (subdistribution hazard model); cause-specific hazard framework earlier | 1983 |
| Kehittäjä≠ | Jason Fine and Robert Gray | Paul Rosenbaum and Donald Rubin |
| Tyyppi≠ | Regression model for time-to-event data with competing events | Method |
| Alkuperäislähde≠ | Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. DOI ↗ | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| Rinnakkaisnimet≠ | competing risks regression, subdistribution hazard model, cause-specific hazard analysis, Fine-Gray model | PSM, propensity score weighting, covariate balance |
| Liittyvät≠ | 4 | 3 |
| Tiivistelmä≠ | Risk-adjusted competing risks analysis extends classical survival analysis to settings where subjects can experience more than one type of terminal event, and where the occurrence of one event prevents the occurrence of another. By modelling cause-specific or subdistribution hazards while adjusting for measured confounders, the method yields unbiased estimates of the absolute probability — the cumulative incidence function — of each event type over time in the presence of competing events. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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