Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mfumo wa Pamoja kwa Data ya Mfumo na Wakati-wa-Kukabiliana na Tukio× | Kikokotozi cha Kuishi cha Kaplan-Meier× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Uhai | Uchanganuzi wa Uhai |
| Familia | Survival analysis | Survival analysis |
| Mwaka wa asili≠ | 2004 | 1958 |
| Mwanzilishi≠ | Tsiatis, A.A. & Davidian, M.; Rizopoulos, D. | Kaplan, E. L. & Meier, P. |
| Aina≠ | Semiparametric regression model | Non-parametric survival estimator |
| Chanzo asilia≠ | Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press. DOI ↗ | Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Majina mbadala≠ | joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model) | product-limit estimator, km curve, kaplan-meier sağkalım analizi |
| Zinazohusiana≠ | 5 | 2 |
| Muhtasari≠ | 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. | The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups. |
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