A Review on Joint Models in Biometrical Research

Neuhaus, Andrea; Augustin, Thomas; Heumann, Christian; Däumer, Martin

doi:10.1080/15598608.2009.10411965

Cited by 11 publications

(11 citation statements)

References 41 publications

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“…Similarly, models are available for heard of HIV/ AIDS campaign [2] (q = 2) and heard of HIV/AIDS responses (q = 3). The joint modeling of these three outcomes has a vector of random effects u distributed as normal with mean vector 0 and covariance matrix, D 123 H∩C , such that the random effects for levels in the hierarchy is If the covariance d qp 1 ¼ 0 then they are uncorrelated, and the resulting model is equivalent to modeling the three outcomes separately [9,10]…”

Section: Simultaneous Modelsmentioning

confidence: 99%

HIV survey in Mozambique: analysis with simultaneous model in contrast to separate hierarchical models

2020

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Background: The analysis of correlated responses obtained one at a time in survey data is not as informative or as useful as modeling them simultaneously. Simultaneous modeling allows for the opportunity to evaluate the system in a more pragmatic form rather than to allow for responses that assumedly originated in isolation. Methods: This research uses the Mozambique National Survey data to demonstrate the benefits of simultaneous modeling on blood test results, knowledge of HIV/AIDS, and awareness of an HIV/AIDS campaign. This simultaneous modeling also addresses the correlation inherent due to the hierarchical structure in the data collection. Results: Employment and self-perceived risk of HIV/AIDS have different impact on blood test, awareness of an HIV/ AIDS campaign, and knowledge of HIV/AIDS when examined simultaneously as opposed to separate modeling. Conclusion: Simultaneous modeling of correlated responses improves the reliability of the estimates. More importantly, it provides an opportunity to engage in cost-saving decisions when designing future surveys and make better health policies.

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Section: Simultaneous Modelsmentioning

confidence: 99%

HIV survey in Mozambique: analysis with simultaneous model in contrast to separate hierarchical models

2020

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“…Research literature for joint models uses the frequentist (or classical) and Bayesian approaches to estimate and predict information of interest (for interesting reviews up to date, see Tsiatis and Davidian, 2004;Neuhaus et al, 2009). In particular, this modelling is relatively new for the Bayesian approach and so it is devoid of some more in-depth research topics, such as sensitivity analysis to the elicitation of prior distributions, sequential update, model validation, and model selection.…”

Section: Joint Models For Longitudinal and Time-to-event Datamentioning

confidence: 99%

Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data

Álvares

Pardo

Forte

et al. 2020

Statistical Modelling

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The statistical analysis of the information generated by medical follow-up is a very important challenge in the field of personalized medicine. As the evolutionary course of a patient's disease progresses, his/her medical follow-up generates more and more information that should be processed immediately in order to review and update his/her prognosis and treatment. Hence, we focus on this update process through sequential inference methods for joint models of longitudinal and time-to-event data from a Bayesian perspective. More specifically, we propose the use of sequential Monte Carlo (SMC) methods for static parameter joint models with the intention of reducing computational time in each update of the full Bayesian inferential process. Our proposal is very general and can be easily applied to most popular joint models approaches. We illustrate the use of the presented sequential methodology in a joint model with competing risk events for a real scenario involving patients on mechanical ventilation in intensive care units (ICUs).

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“…They propose joint models in which longitudinal data are fitted with a mixed-effects model whose random effects are covariates in a generalized linear model (GLM) for the response of interest. If we consider a linear mixed-effects (LME) model for the longitudinal trajectories, several solutions, including likelihood-based and Bayesian approaches, exist ([36,13,23,15,32]; see [27] for an overview).…”

Section: Introductionmentioning

confidence: 99%

Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements

Cruz

Meza

Arribas-Gil

et al. 2016

Journal of Multivariate Analysis

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Joint models for a wide class of response variables and longitudinal measurements consist on a mixed-effects model to fit longitudinal trajectories whose random effects enter as covariates in a generalized linear model for the primary response. They provide a useful way to assess association between these two kinds of data, which in clinical studies are often collected jointly on a series of individuals and may help understanding, for instance, the mechanisms of recovery of a certain disease or the efficacy of a given therapy. When a nonlinear mixed-effects model is used to fit the longitudinal trajectories, the existing estimation strategies based on likelihood approximations have been shown to exhibit some computational efficiency problems (De la Cruz et al., 2011). In this article we consider a Bayesian estimation procedure for the joint model with a nonlinear mixed-effects model for the longitudinal data and a generalized linear model for the primary response. The proposed prior structure allows for the implementation of an MCMC sampler. Moreover, we consider that the errors in the longitudinal model may be correlated. We apply our method to the analysis of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. We also conduct a simulation study to assess the importance of modelling correlated errors and quantify the consequences of model misspecification.

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A Review on Joint Models in Biometrical Research

Cited by 11 publications

References 41 publications

HIV survey in Mozambique: analysis with simultaneous model in contrast to separate hierarchical models

HIV survey in Mozambique: analysis with simultaneous model in contrast to separate hierarchical models

Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data

Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements

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