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.     

Reducing sexual risk and promoting acceptance of men who have sex with men living with HIV in India: Outcomes and process evaluation of a pilot randomised multi-level intervention

ABSTRACT

HIV-positive men who have sex with men (HIV+MSM) in India need culturally-relevant interventions to promote safer sex. We tested a multi-level intervention among HIV+MSM that targeted individual, interpersonal, and community factors, based on the Social-Personal and Social Ecological Models. We conducted a 2?×?2 factorial RCT with 119 HIV+MSM randomised to receive either an individual-level intervention (ILI) using motivational interviewing to promote safer sex, a community-level intervention (CLI) to strengthen community norms toward safer sex and reduce stigma among MSM communities, a multi-level intervention combining the individual- and community-level interventions (ILI?+?CLI), or standard-of-care control. Participants completed pre- and post-intervention assessments of a composite sexual risk score and a process evaluation to assess fidelity and satisfaction. Out of the 119 HIV+MSM, 106 (89.0%) completed pre- and post-intervention assessments. Generalised Estimating Equation models showed that both CLI (Incidence Rate Ratio [IRR]?=?.67, 95% CI .47 to .96) and ILI?+?CLI (IRR?=?.66, 95% CI .48 to .91) groups had a statistically significant decrease in sexual risk compared to the standard-of-care. The interventions had high levels of fidelity and satisfaction. This pilot RCT demonstrated feasibility and potential effectiveness of a multi-level intervention that addresses individual, interpersonal and community-level contributors of sexual risk among HIV+MSM.     

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.     

Qualitative evidence for Resilience,Stress, and Ethnicity (RiSE): A program to address race-based stress among Black women at risk for cardiovascular disease

ObjectiveGrowing evidence demonstrates that perceived discrimination and racism are significant contributing factors to psychological distress, low-grade chronic inflammation, and cardiovascular health disparities among minorities, particularly among Black women. Despite this evidence, there are no evidence-based complementary therapy interventions available to ameliorate chronic stress associated with racism and discrimination. The purpose of this study was to examine the feasibility and effectiveness of a novel, 8-week, group-based stress reduction program, Resilience, Stress and Ethnicity (RiSE), designed to help Black women at risk for cardiovascular disease (CVD) develop effective coping skills for dealing with chronic stress uniquely associated with being a minority.MethodsWe conducted two semi-structured focus groups with Black women (N = 22) following their participation in the 8-week RiSE program. We analyzed the data using constant comparative qualitative methods.ResultsAttrition rate was low (13%) with all participants attending at least 6 of the 8 classes. Participants reported high levels of satisfaction with the program and the majority (81%) reported practicing the skills that they learned in real-life stressful situations. In describing the participants’ response to the program, four key categories emerged from the data: (1) Increasing awareness of stressors associated with perceived discrimination and racism; (2) Coping with race-based stressors; (3) Coping with other sources of stress; and (4) Increasing sense of empowerment and emotion regulation.ConclusionsFindings suggest that RiSE is feasible and effective in helping Black women at risk for CVD cope with chronic stress associated with being a minority. Given evidence that perceived discrimination and racism are underlying factors in many inflammatory-based chronic diseases, this research may have broader implications for reducing health disparities across a wide-spectrum of chronic illnesses in which women minorities are disproportionately affected.     

混合Poisson分布及其应用：疾病的统计分布（五）

本文讨论了混合Ｐｏｉｓｓｏｎ分布的性质、应用条件、参数的估计及混合Ｐｏｉｓｓｏｎ分布阶数的确定，指出混合Ｐｏｉｓｓｏｎ分布可用于混合样本的判别归类，并用Ｂａｙｅｓ的思想导出其判别归类方法。模拟试验结果表明：当混合Ｐｏｉｓｓｏｎ分布中各部分的比例相差不大，而各部分的均值相差较大时，抽样效果和拟合效果越好，所得到的估计值越接近理论值；反之效果越差。