Bayesian joint analysis of heterogeneous- and skewed-longitudinal data and a binary outcome, with application to AIDS clinical studies

2017 ◽  
Vol 27 (10) ◽  
pp. 2946-2963 ◽  
Author(s):  
Xiaosun Lu ◽  
Yangxin Huang ◽  
Jiaqing Chen ◽  
Rong Zhou ◽  
Shuli Yu ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Latent Class ◽  
Joint Modeling ◽  
Finite Mixture ◽  
Joint Analysis ◽  
Binary Outcome ◽  
Class Membership ◽  
Joint Models ◽  
Nonlinear Mixed Effects Models ◽  
Skew Distributions

In medical studies, heterogeneous- and skewed-longitudinal data with mis-measured covariates are often observed together with a clinically important binary outcome. A finite mixture of joint models is currently used to fit heterogeneous-longitudinal data and binary outcome, in which these two parts are connected by the individual latent class membership. The skew distributions, such as skew-normal and skew-t, have shown beneficial in dealing with asymmetric data in various applications in literature. However, there has been relatively few studies concerning joint modeling of heterogeneous- and skewed-longitudinal data and a binary outcome. In this article, we propose a joint model in which a flexible finite mixture of nonlinear mixed-effects models with skew distributions is connected with binary logistic model by a latent class membership indicator. Simulation studies are conducted to assess the performance of the proposed models and method, and a real example from an AIDS clinical trial study illustrates the methodology by modeling the viral dynamics to compare potential models with different distribution specifications; the analysis results are reported.

scholarly journals Joint Modeling in Detecting Predictors of Cd4 Cell Count and Status of Tuberculosis Among HIV Infected Patients Under HAART at Felege Hiwot Teaching and Specialized Hospital, North-west Ethiopia

2020 ◽  
Author(s):  
Setegn Byabil Agegn ◽  
Awoke seyoum Tegegn
Keyword(s):  
Cell Count ◽  
Joint Modeling ◽  
Joint Analysis ◽  
Joint Models ◽  
Expected Number ◽  
Hiv Patients ◽  
Cd4 Cell Count ◽  
North West ◽  
Specialized Hospital ◽  
Cd4 Cell

Abstract Background: Globally, the number of TB patients who had been diagnosed with HIV status reached 2.1 million, which is equivalent to 34 % of notified cases of TB. This research was conducted with the objective to identify potential predictors for the status of TB and CD4 cell count for adult HIV patients at Felege Hiwot Teaching and Specialized Hospital North-west Ethiopia.Methods: A retrospective repeated measures were taken from a sample of 226 HIV patients. Separate and joint models were conducted for data analysis of CD4 cell count and TB status of HIV infected patients. Results: The descriptive statistics indicates that among the HIV patients under HAART, 26.6% had additional TB cases. Hence, the expected number of CD4 cell count of HIV patients who were co-infected with TB was decreased by 2.34 as compared to HIV patients who were free from TB. In joint analysis, age, opportunistic infectious disease, adherence to medication, body mass index and social supports were significantly associated with CD4 cell count and TB status. In addition, one-way interaction terms (time * educational level) was also associated with both outcomes. As patients’ age increased by one year, the expected number of CD4 cell count was decreased by 0.025 cells per/mm3 keeping the other variables constant. The expected number of CD4 cell count for patients whose status were ambulatory was decreased by 3.95 as compared to working status. Both separate and joint modeling approach revealed consistent results for significant predictors. However, joint models were more adequate and efficient. Conclusions: Among the predictors of CD4 cell count and TB status, WHO stages, age of patients, functional status of patients, hemoglobin level and residence area were significant predictors for the variable of interests. More attention should be given for HIV/TB co- infected ambulatory and bedridden patients.

scholarly journals Joint modeling versus discriminant analysis for dynamic prediction of a binary outcome based on longitudinal data: A simulation study

2020 ◽  
Author(s):  
Rana Dandis ◽  
Joanna IntHout ◽  
Kit Roes ◽  
Steven Teerenstra
Keyword(s):  
Discriminant Analysis ◽  
Longitudinal Data ◽  
Simulation Study ◽  
Joint Modeling ◽  
Binary Outcome ◽  
Dynamic Prediction

Abstract The authors have withdrawn this preprint due to erroneous posting.

Mixed-effects location and scale Tobit joint models for heterogeneous longitudinal data with skewness, detection limits, and measurement errors

2017 ◽  
Vol 27 (12) ◽  
pp. 3525-3543
Author(s):  
Tao Lu
Keyword(s):  
Longitudinal Data ◽  
Measurement Errors ◽  
Limit Of Detection ◽  
Joint Modeling ◽  
Research Area ◽  
Joint Model ◽  
Mixed Effects ◽  
Joint Models ◽  
Active Research ◽  
Aids Study

The joint modeling of mean and variance for longitudinal data is an active research area. This type of model has the advantage of accounting for heteroscedasticity commonly observed in between and within subject variations. Most of researches focus on improving the estimating efficiency but ignore many data features frequently encountered in practice. In this article, we develop a mixed-effects location scale joint model that concurrently accounts for longitudinal data with multiple features. Specifically, our joint model handles heterogeneity, skewness, limit of detection, measurement errors in covariates which are typically observed in the collection of longitudinal data from many studies. We employ a Bayesian approach for making inference on the joint model. The proposed model and method are applied to an AIDS study. Simulation studies are performed to assess the performance of the proposed method. Alternative models under different conditions are compared.

scholarly journals E-bike user groups and substitution effects: evidence from longitudinal travel data in the Netherlands

2021 ◽  
Author(s):  
Mathijs de Haas ◽  
Maarten Kroesen ◽  
Caspar Chorus ◽  
Sascha Hoogendoorn-Lanser ◽  
Serge Hoogendoorn
Keyword(s):  
The Netherlands ◽  
Longitudinal Data ◽  
Latent Class ◽  
Good Alternative ◽  
Substitution Effects ◽  
Or Education ◽  
Cross Sectional ◽  
User Groups ◽  
Using Data ◽  
Mode Of Transport

AbstractIn recent years, the e-bike has become increasingly popular in many European countries. With higher speeds and less effort needed, the e-bike is a promising mode of transport to many, and it is considered a good alternative for certain car trips by policy-makers and planners. A major limitation of many studies that investigate such substitution effects of the e-bike, is their reliance on cross-sectional data which do not allow an assessment of within-person travel mode changes. As a consequence, there is currently no consensus about the e-bike’s potential to replace car trips. Furthermore, there has been little research focusing on heterogeneity among e-bike users. In this respect, it is likely that different groups exist that use the e-bike for different reasons (e.g. leisure vs commute travel), something which will also influence possible substitution patterns. This paper contributes to the literature in two ways: (1) it presents a statistical analysis to assess the extent to which e-bike trips are substituting trips by other travel modes based on longitudinal data; (2) it reveals different user groups among the e-bike population. A Random Intercept Cross-Lagged Panel Model is estimated using five waves of data from the Netherlands Mobility Panel. Furthermore, a Latent Class Analysis is performed using data from the Dutch national travel survey. Results show that, when using longitudinal data, the substitution effects between e-bike and the competing travel modes of car and public transport are not as significant as reported in earlier research. In general, e-bike trips only significantly reduce conventional bicycle trips in the Netherlands, which can be regarded an unwanted effect from a policy-viewpoint. For commuting, the e-bike also substitutes car trips. Furthermore, results show that there are five different user groups with their own distinct behaviour patterns and socio-demographic characteristics. They also show that groups that use the e-bike primarily for commuting or education are growing at a much higher rate than groups that mainly use the e-bike for leisure and shopping purposes.

Health Care Utilization, Transition Readiness, and Quality of Life: A Latent Class Analysis

2020 ◽  
Author(s):  
Katherine A Traino ◽  
Christina M Sharkey ◽  
Megan N Perez ◽  
Dana M Bakula ◽  
Caroline M Roberts ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Health Care ◽  
Health Care Utilization ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Care Utilization ◽  
Class Membership ◽  
Transition Readiness ◽  
High Utilization

Abstract Objective To identify possible subgroups of health care utilization (HCU) patterns among adolescents and young adults (AYAs) with a chronic medical condition (CMC), and examine how these patterns relate to transition readiness and health-related quality of life (HRQoL). Methods Undergraduates (N = 359; Mage=19.51 years, SD = 1.31) with a self-reported CMC (e.g., asthma, allergies, irritable bowel syndrome) completed measures of demographics, HCU (e.g., presence of specialty or adult providers, recent medical visits), transition readiness, and mental HRQoL (MHC) and physical HRQoL (PHC). Latent class analysis identified four distinct patterns of HCU. The BCH procedure evaluated how these patterns related to transition readiness and HRQoL outcomes. Results Based on seven indicators of HCU, a four-class model was found to have optimal fit. Classes were termed High Utilization (n = 95), Adult Primary Care Physician (PCP)-Moderate Utilization (n = 107), Family PCP-Moderate Utilization (n = 81), and Low Utilization (n = 76). Age, family income, and illness controllability predicted class membership. Class membership predicted transition readiness and PHC, but not MHC. The High Utilization group reported the highest transition readiness and the lowest HRQoL, while the Low Utilization group reported the lowest transition readiness and highest HRQoL. Conclusions The present study characterizes the varying degrees to which AYAs with CMCs utilize health care. Our findings suggest poorer PHC may result in higher HCU, and that greater skills and health care engagement may not be sufficient for optimizing HRQoL. Future research should examine the High Utilization subgroup and their risk for poorer HRQoL.

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