Structured analysis of the high-dimensional FMR model

Abstract(#br)The finite mixture of regression (FMR) model is a popular tool for accommodating data heterogeneity. In the analysis of FMR models with high-dimensional covariates, it is necessary to conduct regularized estimation and identify important covariates rather than noises. In the literature, there has been a lack of attention paid to the differences among important covariates, which can lead to the underlying structure of covariate effects. Specifically, important covariates can be classified into two types: those that behave the same in different subpopulations and those that behave differently. It is of interest to conduct structured analysis to identify such structures, which will enable researchers to better understand covariates and their associations with outcomes. Specifically, the FMR model with high-dimensional covariates is considered. A structured penalization approach is developed for regularized estimation, selection of important variables, and, equally importantly, identification of the underlying covariate effect structure. The proposed approach can be effectively realized, and its statistical properties are rigorously established. Simulation demonstrates its superiority over alternatives. In the analysis of cancer gene expression data, interesting models/structures missed by the existing analysis are identified

Comparative Analysis of Social Support in Online Health Communities Using a Word Co-Occurrence Network Analysis Approach

Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with others facing similar health problems and receive multiple types of social support, including but not limited to informational support, emotional support, and companionship. The aim of this study is to examine the differences in social support communication among people with different types of cancers. A novel approach is developed to better understand the types of social support embedded in OHC posts. Our approach, based on the word co-occurrence network analysis, preserves the semantic structures of the texts. Information extraction from the semantic structures is supported by the interplay of quantitative and qualitative analyses of the network structures. Our analysis shows that significant differences in social support exist across cancer types, and evidence for the differences across diseases in terms of communication preferences and language use is also identified. Overall, this study can establish a new venue for extracting and analyzing information, so as to inform social support for clinical care

Sleep Quality of Functional Gastrointestinal Disorder Patients in Class-Three Hospitals: A Cross-Sectional Study in Tianjin, China

Background. Functional gastrointestinal disorder (FGID) patients are influenced by anxiety, depression, and low sleep quality, which reduce the quality of their life. However, epidemiological data on the quality of sleep in FGID patients were lacking. This study aims to explore the sleep quality and influencing factors of the sleep quality in FGID patients. Methods. 1200 subjects, diagnosed as FGID in one of the six class-three hospitals in Tianjin, China, from January to December 2014, were recruited. The information about demographic information, the severity of clinical symptoms, psychological status (Zung self-rating depression scale), and sleep quality (evaluated with Pittsburgh sleep quality index) was gathered. Results. The questionnaires from 1117 participants were collected including 920 of functional dyspepsia (FD) patients, 77 of irritable bowel disease (IBS) patients, 26 of functional constipation (FC) patients, and 94 other FGID patients. The results showed that morbidity rate for FD patients who had sleep disorders was higher than those who suffered from IBS or FC (P<0.001). The proportion of elderly patients suffering from low sleep quality was higher than that of middle-aged and young patients (P<0.001). The binary logistic regression analysis showed that age, education, and the severity of FGID symptom were influencing factors for poor sleep quality in FGID patients. Conclusion. The issue of poor sleep quality in FGID patients in Tianjin area is prominent, and elderly patients suffer lower sleep quality than other FGID patients. Age, education, and the severity of FGID symptoms are critical influencing factors which result in a drop-in sleep quality