Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

Stature estimation for Saudi men based on different combinations of upper limb part dimensions

Estimating stature based on body/limb parts can help define the characteristics of unidentified bodies. The most studied upper limb part is the hand, although few studies have examined whether stature can be estimated using fingers plus other hand dimensions. Moreover, there is paucity in anthropometric studies that determined whether bilateral whole limb parts (e.g., arms, forearms, and hands) are related to stature among the living subjects.This prospective cross-sectional study aimed to evaluate the relationship between different upper limb measurements and the stature of Saudi men. Furthermore, I assessed whether upper limb asymmetry was present, and developed regression models to estimate stature based on different available measurements. Stature and 13 upper limb parameters were measured for 100 right-handed Saudi men who were 18 to 24 years old.All measurements were positively correlated with stature (P < .001), and the best single predictor was the bilateral ulnar length. Asymmetry was more pronounced in the hand measurements. A multiparameter model provided reasonable predictive accuracy (±3.77–5.68 cm) and was more accurate than single-parameter models. Inclusion of the right-side fingers improved the model''s accuracy.This study developed potential models for estimating stature during the identification of bodies of Saudi men.     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.     

2009-2019年西安市肺结核流行特征及治疗转归分析

目的 了解2009-2019年西安市肺结核的流行特征及治疗转归情况，为完善西安市肺结核防治策略提供依据。方法 收集2009-2019年《传染病信息管理系统》中登记的肺结核患者病案信息数据，通过描述性研究方法对肺结核流行病学特征和治疗转归情况进行分析。结果 2009-2019年间西安市肺结核年平均发病率为49.06/10万。郊县、郊区、城区肺结核年平均发病率分别为54.13/10万、47.46/10万和46.19/10万；男性发病率是女性的1.85倍。职业分布排在前5位的为农民（47.48%），家政、家务及待业（14.65%），离退人员（9.55%），学生（8.63%）和工人（5.23%）。利福平敏感或耐药性未知患者的成功治疗率平均为98.03%，利福平耐药患者治疗成功率为51.47%。结论 西安市近年来肺结核发病率和治疗率呈增长趋势，利福平耐药患者治疗成功率较低，要加强对重点人群的健康促进工作，加大肺结核发现力度及患者治疗管理工作。     

Validation of PATHFx 2.0: An open-source tool for estimating survival in patients undergoing pathologic fracture fixation

Treatment decisions in patients with metastatic bone disease rely on accurate survival estimation. We developed the original PATHFx models using expensive, proprietary software and now seek to provide a more cost-effective solution. Using open-source machine learning software to create PATHFx version 2.0, we asked whether PATHFx 2.0 could be created using open-source methods and externally validated in two unique patient populations. The training set of a well-characterized, database records of 189 patients and the bnlearn package within R Version 3.5.1 (R Foundation for Statistical Computing), was used to establish a series of Bayesian belief network models designed to predict survival at 1, 3, 6, 12, 18, and 24 months. Each was externally validated in both a Scandinavian (n = 815 patients) and a Japanese (n = 261 patients) data set. Brier scores and receiver operating characteristic curves to assessed discriminatory ability. Decision curve analysis (DCA) evaluated whether models should be used clinically. DCA showed that the model should be used clinically at all time points in the Scandinavian data set. For the 1-month time point, DCA of the Japanese data set suggested to expect better outcomes assuming all patients will survive greater than 1 month. Brier scores for each curve demonstrate that the models are accurate at each time point. Statement of Clinical Significance: we successfully transitioned to PATHFx 2.0 using open-source software and externally validated it in two unique patient populations, which can be used as a cost-effective option to guide surgical decisions in patients with metastatic bone disease.     

Mise à jour des croyances et congruence à l'humeur dans le trouble dépressif

Depressive disorder is characterized by a polymorphic symptomatology associating emotional, cognitive and behavioral disturbances. One of the most specific symptoms is negative beliefs, called congruent to mood. Despite the importance of these beliefs in the development, the maintenance, and the recurrence of depressive episodes, little is known about the processes underlying the generation of depressive beliefs. In this paper, we detail the link between belief updating mechanisms and the genesis of depressive beliefs. We show how depression alters information processing, generating cognitive immunization when processing positive information, affective updating bias related to the valence of belief and prediction error, and difficultie to disengage from negative information. We suggest that disruption of belief-updating mechanisms forms the basis of belief-mood congruence in depression.