145 related articles for article (PubMed ID: 34018218)
1. Backward joint model and dynamic prediction of survival with multivariate longitudinal data.
Shen F; Li L
Stat Med; 2021 Sep; 40(20):4395-4409. PubMed ID: 34018218
[TBL] [Abstract][Full Text] [Related]
2. Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker.
Pickett KL; Suresh K; Campbell KR; Davis S; Juarez-Colunga E
BMC Med Res Methodol; 2021 Oct; 21(1):216. PubMed ID: 34657597
[TBL] [Abstract][Full Text] [Related]
3. Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations.
Rustand D; van Niekerk J; Krainski ET; Rue H; Proust-Lima C
Biostatistics; 2024 Apr; 25(2):429-448. PubMed ID: 37531620
[TBL] [Abstract][Full Text] [Related]
4. Joint modelling of longitudinal measurements and survival times via a multivariate copula approach.
Zhang Z; Charalambous C; Foster P
J Appl Stat; 2023; 50(13):2739-2759. PubMed ID: 37720246
[TBL] [Abstract][Full Text] [Related]
5. Joint modeling of survival time and longitudinal outcomes with flexible random effects.
Choi J; Zeng D; Olshan AF; Cai J
Lifetime Data Anal; 2018 Jan; 24(1):126-152. PubMed ID: 28856493
[TBL] [Abstract][Full Text] [Related]
6. Standard error estimation using the EM algorithm for the joint modeling of survival and longitudinal data.
Xu C; Baines PD; Wang JL
Biostatistics; 2014 Oct; 15(4):731-44. PubMed ID: 24771699
[TBL] [Abstract][Full Text] [Related]
7. A flexible joint model for multiple longitudinal biomarkers and a time-to-event outcome: With applications to dynamic prediction using highly correlated biomarkers.
Li N; Liu Y; Li S; Elashoff RM; Li G
Biom J; 2021 Dec; 63(8):1575-1586. PubMed ID: 34272887
[TBL] [Abstract][Full Text] [Related]
8. Joint latent class models for longitudinal and time-to-event data: a review.
Proust-Lima C; Séne M; Taylor JM; Jacqmin-Gadda H
Stat Methods Med Res; 2014 Feb; 23(1):74-90. PubMed ID: 22517270
[TBL] [Abstract][Full Text] [Related]
9. Longitudinal latent variable models given incompletely observed biomarkers and covariates.
Ren C; Shin Y
Stat Med; 2016 Nov; 35(26):4729-4745. PubMed ID: 27377366
[TBL] [Abstract][Full Text] [Related]
10. A Copula Approach to Joint Modeling of Longitudinal Measurements and Survival Times Using Monte Carlo Expectation-Maximization with Application to AIDS Studies.
Ganjali M; Baghfalaki T
J Biopharm Stat; 2015; 25(5):1077-99. PubMed ID: 25372017
[TBL] [Abstract][Full Text] [Related]
11. A comparison of two approaches to dynamic prediction: Joint modeling and landmark modeling.
Li W; Li L; Astor BC
Stat Med; 2023 Jun; 42(13):2101-2115. PubMed ID: 36938960
[TBL] [Abstract][Full Text] [Related]
12. joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes.
Hickey GL; Philipson P; Jorgensen A; Kolamunnage-Dona R
BMC Med Res Methodol; 2018 Jun; 18(1):50. PubMed ID: 29879902
[TBL] [Abstract][Full Text] [Related]
13. Gaussian variational approximate inference for joint models of longitudinal biomarkers and a survival outcome.
Tu J; Sun J
Stat Med; 2023 Feb; 42(3):316-330. PubMed ID: 36443903
[TBL] [Abstract][Full Text] [Related]
14. On longitudinal prediction with time-to-event outcome: Comparison of modeling options.
Maziarz M; Heagerty P; Cai T; Zheng Y
Biometrics; 2017 Mar; 73(1):83-93. PubMed ID: 27438160
[TBL] [Abstract][Full Text] [Related]
15. Joint analysis of multivariate interval-censored survival data and a time-dependent covariate.
Wu D; Li C
Stat Methods Med Res; 2021 Mar; 30(3):769-784. PubMed ID: 33256555
[TBL] [Abstract][Full Text] [Related]
16. Dynamic prediction of competing risk events using landmark sub-distribution hazard model with multiple longitudinal biomarkers.
Wu C; Li L; Li R
Stat Methods Med Res; 2020 Nov; 29(11):3179-3191. PubMed ID: 32419611
[TBL] [Abstract][Full Text] [Related]
17. Extending the code in the open-source saemix package to fit joint models of longitudinal and time-to-event data.
Lavalley-Morelle A; Mentré F; Comets E; Mullaert J
Comput Methods Programs Biomed; 2024 Apr; 247():108095. PubMed ID: 38422892
[TBL] [Abstract][Full Text] [Related]
18. An approximate joint model for multiple paired longitudinal outcomes and time-to-event data.
Elmi AF; Grantz KL; Albert PS
Biometrics; 2018 Sep; 74(3):1112-1119. PubMed ID: 29492955
[TBL] [Abstract][Full Text] [Related]
19. Fitting joint models of longitudinal observations and time to event by sequential Bayesian updating.
McKeigue P
Stat Methods Med Res; 2022 Oct; 31(10):1934-1941. PubMed ID: 35642267
[TBL] [Abstract][Full Text] [Related]
20. Estimation in multivariate
Taavoni M; Arashi M
Biom J; 2022 Mar; 64(3):539-556. PubMed ID: 34821410
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
