Joint Modelling of Repeated Measures and Survival Time Data

Ha, Il Do; Park, Taesung; Lee, Youngjo

doi:10.1002/bimj.200390039

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…In our case study, as it was expected, the level of creatinine and BUN can be used to monitoring posttransplant renal graft function. In fact, the levels of these biomarkers will increase in patients with a dysfunctional graft, and this increase is translated to a higher risk of rejection or mortality [20][21][22][23]. On the other hand, less hemoglobin level in the post-transplant period has been known to be associated with an increased risk of graft loss and mortality [24,25] that was consistent with our findings.…”

Section: Data Availabilitysupporting

confidence: 89%

Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

Alafchi

Mahjub

Tapak

et al. 2021

Journal of Probability and Statistics

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In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time until an event such as recovery, disease relapse, or death occurs. Joint modeling approaches are increasingly used to study the association between one longitudinal and one survival outcome. However, in practice, a patient may experience multiple disease progression events successively. So instead of modeling of a single event, progression of the disease as a multistate process should be modeled. On the other hand, in such studies, multivariate longitudinal outcomes may be collected and their association with the survival process is of interest. In the present study, we applied a joint model of various longitudinal biomarkers and transitions between different health statuses in patients who underwent renal transplantation. The full joint likelihood approaches are faced with the complexities in computation of the likelihood. So, here, we have proposed two-stage modeling of multivariate longitudinal outcomes and multistate conditions to avoid these complexities. The proposed model showed reliable results compared to the joint model in case of joint modeling of univariate longitudinal biomarker and the multistate process.

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Section: Data Availabilitysupporting

confidence: 89%

Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

Alafchi

Mahjub

Tapak

et al. 2021

Journal of Probability and Statistics

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“…The h ‐likelihood approach has been extended to joint DHGLMs for multivariate responses. Ha et al. (2003) considered the use of joint modelling of repeated measures and survival times, and Yun and Lee (2004a) that for continuous and binary responses.…”

Section: Extensionsmentioning

confidence: 99%

Double Hierarchical Generalized Linear Models (With Discussion)

Lee

Nelder

2006

Journal of the Royal Statistical Society Series C: Applied Statistics

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311

577

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Summary. We propose a class of double hierarchical generalized linear models in which random effects can be specified for both the mean and dispersion. Heteroscedasticity between clusters can be modelled by introducing random effects in the dispersion model, as is heterogeneity between clusters in the mean model. This class will, among other things, enable models with heavy-tailed distributions to be explored, providing robust estimation against outliers. The h-likelihood provides a unified framework for this new class of models and gives a single algorithm for fitting all members of the class. This algorithm does not require quadrature or prior probabilities.

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“…In Henderson et al [19], a latent bivariate Gaussian process is introduced as a time-dependent variable in a proportional hazard model. Multivariate generalizations of such methods and the estimation procedures have been suggested recently [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Stochastic model for analysis of longitudinal data on aging and mortality

Yashin

Arbeev

Akushevich

et al. 2007

Mathematical Biosciences

133

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Aging-related changes in a human organism follow dynamic regularities, which contribute to the observed age patterns of incidence and mortality curves. An organism's "optimal" (normal) physiological state changes with age, affecting the values of risks of disease and death. The resistance to stresses, as well as adaptive capacity, declines with age. An exposure to improper environment results in persisting deviation of individuals' physiological (and biological) indices from their normal state (due to allostatic adaptation), which, in turn, increases chances of disease and death. Despite numerous studies investigating these effects, there is no conceptual framework, which would allow for putting all these findings together, and analyze longitudinal data taking all these dynamic connections into account. In this paper we suggest such a framework, using a new version of stochastic process model of aging and mortality. Using this model, we elaborated a statistical method for analyses of longitudinal data on aging, health and longevity and tested it using different simulated data sets. The results show that the model may characterize complicated interplay among different components of aging-related changes in humans and that the model parameters are identifiable from the data.

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Joint Modelling of Repeated Measures and Survival Time Data

Cited by 11 publications

References 19 publications

Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

Double Hierarchical Generalized Linear Models (With Discussion)

Stochastic model for analysis of longitudinal data on aging and mortality

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