A joint model for mixed longitudinal k-category inflation ordinal and continuous responses

Statistics ◽  
2021 ◽  
pp. 1-26
Author(s):  
Nastaran Sharifian ◽  
Ehsan Bahrami Samani
Keyword(s):  
Joint Model ◽  
Continuous Responses

scholarly journals Marginalized Two-Part Joint Modeling of Longitudinal Semi-Continuous Responses and Survival Data: With Application to Medical Costs

Mathematics ◽  
2021 ◽  
Vol 9 (20) ◽  
pp. 2603
Author(s):  
Mohadeseh Shojaei Shahrokhabadi ◽  
(Din) Ding-Geng Chen ◽  
Sayed Jamal Mirkamali ◽  
Anoshirvan Kazemnejad ◽  
Farid Zayeri
Keyword(s):  
Survival Data ◽  
Joint Modeling ◽  
Medical Costs ◽  
Joint Model ◽  
Superior Performance ◽  
Parameter Estimates ◽  
Joint Models ◽  
Survival Status ◽  
Continuous Responses ◽  
Zero Values

Non-negative continuous outcomes with a substantial number of zero values and incomplete longitudinal follow-up are quite common in medical costs data. It is thus critical to incorporate the potential dependence of survival status and longitudinal medical costs in joint modeling, where censorship is death-related. Despite the wide use of conventional two-part joint models (CTJMs) to capture zero-inflation, they are limited to conditional interpretations of the regression coefficients in the model’s continuous part. In this paper, we propose a marginalized two-part joint model (MTJM) to jointly analyze semi-continuous longitudinal costs data and survival data. We compare it to the conventional two-part joint model (CTJM) for handling marginal inferences about covariate effects on average costs. We conducted a series of simulation studies to evaluate the superior performance of the proposed MTJM over the CTJM. To illustrate the applicability of the MTJM, we applied the model to a set of real electronic health record (EHR) data recently collected in Iran. We found that the MTJM yielded a smaller standard error, root-mean-square error of estimates, and AIC value, with unbiased parameter estimates. With this MTJM, we identified a significant positive correlation between costs and survival, which was consistent with the simulation results.

A Joint Model for Quotation Attribution and Coreference Resolution

2014 ◽  
Author(s):  
Mariana S. C. Almeida ◽  
Miguel B. Almeida ◽  
André F. T. Martins
Keyword(s):  
Joint Model ◽  
Coreference Resolution

Going Hybrid: A Joint Model for Temperature and Natural Gas

2015 ◽  
Author(s):  
Roberto Baviera ◽  
Teodoro Federico Mainetti
Keyword(s):  
Natural Gas ◽  
Joint Model

scholarly journals Flexible multivariate joint model of longitudinal intensity and binary process for medical monitoring of frequently collected data

2021 ◽  
Author(s):  
Resmi Gupta ◽  
Jane C. Khoury ◽  
Mekibib Altaye ◽  
Roman Jandarov ◽  
Rhonda D. Szczesniak
Keyword(s):  
Joint Model ◽  
Medical Monitoring ◽  
Binary Process

scholarly journals Competing risks joint models using R-INLA

2021 ◽  
Vol 21 (1-2) ◽  
pp. 56-71
Author(s):  
Janet van Niekerk ◽  
Haakon Bakka ◽  
Håvard Rue
Keyword(s):  
Competing Risks ◽  
Count Data ◽  
Joint Model ◽  
Point Of View ◽  
Spatial Structures ◽  
Joint Models ◽  
Hazard Functions ◽  
Longitudinal Count Data ◽  
Non Gaussian ◽  
Made In

The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this article, we provide a framework based on R-INLA to apply competing risks joint models in a unifying way such that non-Gaussian longitudinal data, spatial structures, times-dependent splines and various latent association structures, to mention a few, are all embraced in our approach. Our motivation stems from the SANAD trial which exhibits non-linear longitudinal trajectories and competing risks for failure of treatment. We also present a discrete competing risks joint model for longitudinal count data as well as a spatial competing risks joint model as specific examples.

scholarly journals A Multidimensional Item Response Theory Model for Continuous and Graded Responses With Error in Persons and Items

2021 ◽  
pp. 001316442199841
Author(s):  
Pere J. Ferrando ◽  
David Navarro-González
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Theory Model ◽  
Response Model ◽  
Response Theory ◽  
Continuous Response ◽  
Graded Responses ◽  
Graded Response ◽  
Continuous Responses ◽  
Differential Measurement Error

Item response theory “dual” models (DMs) in which both items and individuals are viewed as sources of differential measurement error so far have been proposed only for unidimensional measures. This article proposes two multidimensional extensions of existing DMs: the M-DTCRM (dual Thurstonian continuous response model), intended for (approximately) continuous responses, and the M-DTGRM (dual Thurstonian graded response model), intended for ordered-categorical responses (including binary). A rationale for the extension to the multiple-content-dimensions case, which is based on the concept of the multidimensional location index, is first proposed and discussed. Then, the models are described using both the factor-analytic and the item response theory parameterizations. Procedures for (a) calibrating the items, (b) scoring individuals, (c) assessing model appropriateness, and (d) assessing measurement precision are finally discussed. The simulation results suggest that the proposal is quite feasible, and an illustrative example based on personality data is also provided. The proposals are submitted to be of particular interest for the case of multidimensional questionnaires in which the number of items per scale would not be enough for arriving at stable estimates if the existing unidimensional DMs were fitted on a separate-scale basis.

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