Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Kopīgais modelis longitudināliem datiem un notikuma iestāšanās laikam× | Jaukto efektu modelis× | |
|---|---|---|
| Nozare≠ | Dzīvildze | Statistika |
| Saime≠ | Survival analysis | Regression model |
| Izcelsmes gads≠ | 2004 | 1982 |
| Autors≠ | Tsiatis, A.A. & Davidian, M.; Rizopoulos, D. | Laird & Ware |
| Tips≠ | Semiparametric regression model | Mixed effects regression |
| Pirmavots≠ | Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press. DOI ↗ | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ |
| Citi nosaukumi | joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model) | LME, LMM, mixed model, random effects model |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | The joint model for longitudinal and time-to-event data, formalised by Tsiatis and Davidian in 2004 and extended comprehensively by Rizopoulos in 2012, simultaneously estimates a mixed-effects model for repeatedly measured biomarkers and a survival model for the time to an event, linking the two processes through shared random effects. It resolves two major problems that simpler approaches cannot handle: informative dropout from longitudinal studies and the endogeneity of time-varying biomarkers used as covariates in a Cox model. | A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated. |
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