A joint model for multivariate longitudinal and survival data to discover the conversion to Alzheimer's disease

Song

2022

Stat Methods Med Res

Self Cite

We propose a joint modeling approach to investigate the observed and latent risk factors of the multivariate failure times of interest. The proposed model comprises two parts. The first part is a distribution-free confirmatory factor analysis model that characterizes the latent factors by correlated multiple observed variables. The second part is a multivariate additive hazards model that assesses the observed and latent risk factors of the failure times. A hybrid procedure that combines the borrow-strength estimation approach and the asymptotically distribution-free generalized least square method is developed to estimate the model parameters. The asymptotic properties of the proposed estimators are derived. Simulation studies demonstrate that the proposed method performs well for practical settings. An application to a study concerning the risk factors of multiple diabetic complications is provided.

Section: Discussionmentioning

confidence: 99%

Joint analysis of multivariate failure time data with latent variables

Song

2022

Stat Methods Med Res

Self Cite

“…As for longitudinal data, the most used model is the joint model. For example, Kang et al used the joint model to discover the conversion to Alzheimer’s disease [ 44 ]. In this paper, we first used the backward joint model (BJM), a new algorithm that has recently been proposed, so there are limited examples of its use in recent cases.…”

Section: Discussionmentioning

confidence: 99%

Longitudinal Data to Enhance Dynamic Stroke Risk Prediction

Zheng

Chen

2022

Healthcare

Stroke risk prediction based on electronic health records is currently an important research topic. Previous research activities have generally used single-time physiological data to build static models and have focused on algorithms to improve prediction accuracy. Few studies have considered historical measurements from a data perspective to construct dynamic models. Since it is a chronic disease, the risk of having a stroke increases and the corresponding risk factors become abnormal when healthy people are diagnosed with a stroke. Therefore, in this paper, we applied longitudinal data, with the backward joint model, to the Chinese Longitudinal Healthy Longevity and Happy Family Study’s dataset to monitor changes in individuals’ health status precisely on time and to increase the prediction accuracy of the model. The three-year prediction accuracy of our model, considering three measurements of longitudinal parameters, is 0.926. This is higher than the traditional Cox proportional hazard model, which has a 0.833 prediction accuracy. The results obtained in this study verified that longitudinal data improves stroke risk prediction accuracy and is promising for dynamic stroke risk prediction and prevention. Our model also verified that the frequency of fruit consumption, erythrocyte hematocrit, and glucose are potential stroke-related factors.

“…The results of these models provided valuable information to guide public health policies, often on a national scale (9). Authors such as Kang et al (10) developed a model multivariate analysis to investigate the risk factors of the conversion from mild cognitive impairment to Alzheimer's disease and predict the time to onset of disease. These authors used the factor analytic technique to comprehensively characterize patients' cognitive impairment through multiple assessments of cognitive ability.…”

Section: Introductionmentioning

confidence: 99%

Multivariate analysis in data science for the geospatial distribution of the breast cancer mortality rate in Colombia

et al. 2023

This research is framed in the area of biomathematics and contributes to the epidemiological surveillance entities in Colombia to clarify how breast cancer mortality rate (BCM) is spatially distributed in relation to the forest area index (FA) and circulating vehicle index (CV). In this regard, the World Health Organization has highlighted the scarce generation of knowledge that relates mortality from tumor diseases to environmental factors. Quantitative methods based on geospatial data science are used with cross-sectional information from the 2018 census; it’s found that the BCM in Colombia is not spatially randomly distributed, but follows cluster aggregation patterns. Under multivariate modeling methods, the research provides sufficient statistical evidence in terms of not rejecting the hypothesis that if a spatial unit has high FA and low CV, then it has significant advantages in terms of lower BCM.