Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Baijesa konkurences risku analīze× | Daudzstāvokļu izdzīvošanas modelis× | |
|---|---|---|
| Nozare≠ | Epidemioloģija | Dzīvildze |
| Saime≠ | Process / pipeline | Survival analysis |
| Izcelsmes gads≠ | 1980s–2000s (classical CR: 1970s; Bayesian extension: 1990s–2000s) | 1978 |
| Autors≠ | Various; Bayesian formulation advanced by Gelfand, Dey, Larson, and Dinse among others | Andersen, P.K. & Keiding, N. (foundational framework); popularised by Putter, Fiocco & Geskus (2007) |
| Tips≠ | Bayesian survival/time-to-event model | Semi-parametric hazard model |
| Pirmavots≠ | Larson, M. G., & Dinse, G. E. (1985). A mixture model for the regression analysis of competing risks data. Applied Statistics, 34(3), 201–211. DOI ↗ | Putter, H., Fiocco, M. & Geskus, R.B. (2007). Tutorial in Biostatistics: Competing Risks and Multi-State Models. Statistics in Medicine, 26(11), 2389–2430. DOI ↗ |
| Citi nosaukumi≠ | Bayesian cause-specific hazard model, Bayesian subdistribution hazard model, BCRA, Bayesian cumulative incidence analysis | illness-death model, multi-state transition model, Çok Durumlu Model (Multi-State / Illness-Death) |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | Bayesian competing risks analysis is a time-to-event method for settings where subjects can fail from more than one mutually exclusive cause — such as death from cancer versus death from cardiovascular disease — and prior knowledge or small-sample uncertainty makes a Bayesian framework advantageous. It extends classical competing risks models (cause-specific hazards and cumulative incidence functions) by placing probability distributions over unknown parameters and updating those distributions with observed data, yielding full posterior inference for each failure type. | The multi-state model is a generalised survival framework, formalised in the work of Andersen and Keiding and brought to wide biostatistical practice by Putter, Fiocco and Geskus (2007), that models individuals moving through multiple distinct health states — for example, healthy, ill and dead — over time. A separate hazard function is estimated for each possible transition, and transition probabilities are recovered via the product-integral of the cumulative transition intensities. |
| ScholarGateDatu kopa ↗ |
|
|