Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Fleksibel parametrisk overlevelsesmodell (Royston-Parmar)× | Fine-Gray-modellen for konkurrerende risiko× | |
|---|---|---|
| Fagfelt≠ | Overlevelsesanalyse | Statistikk |
| Familie≠ | Survival analysis | Hypothesis test |
| Opprinnelsesår≠ | 2002 | 1999 |
| Opphavsperson≠ | Royston, P. & Parmar, M.K.B. | Jason P. Fine & Robert J. Gray |
| Type≠ | Parametric survival regression model | Subdistribution hazard regression |
| Opprinnelig kilde≠ | Royston, P. & Parmar, M.K.B. (2002). Flexible Parametric Proportional-Hazards and Proportional-Odds Models for Censored Survival Data, with Application to Prognostic Modelling and Estimation of Treatment Effects. Statistics in Medicine, 21(15), 2175–2197. DOI ↗ | 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 ↗ |
| Alias | flexible parametric model, restricted cubic spline survival model, stpm2, Esnek Parametrik Survival Modeli (Royston-Parmar) | competing risks regression, subdistribution hazard model, Fine-Gray model, Fine-Gray Competing Risks Modeli |
| Relaterte≠ | 8 | 5 |
| Sammendrag≠ | The Royston-Parmar model, introduced by Royston and Parmar in 2002, is a modern parametric approach to survival analysis that replaces the rigid distributional assumptions of classical models with a restricted cubic spline fitted to the log-cumulative-hazard scale. It combines the interpretability of a fully parametric model with the flexibility to capture non-standard hazard shapes, and it supports proportional-hazards, accelerated failure-time, and proportional-odds link functions. | The Fine-Gray model is a semiparametric regression method for survival data in which two or more mutually exclusive event types compete to occur first. Proposed by Fine and Gray in 1999, it models the subdistribution hazard of each event type directly, allowing covariates to be linked to the cumulative incidence function (CIF) — the quantity that actually answers 'what is the probability of experiencing event type k by time t?'. It corrects the well-known shortcoming of standard Cox regression, which ignores competing events and thereby overestimates cause-specific probabilities. |
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