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.     

Use of Alternative Designs and Data Sources for Pediatric Trials

Abstract

Children are considered a vulnerable group and as such are granted additional protection as research subjects. Research projects using children as research subjects are justifiable if the answer to the scientific question of the study cannot be obtained by enrolling adult subjects (cf. scientific necessity). Thus, there is an ethical obligation to explore innovative analytical strategies that seek balance between the feasibility of conducting a trial and maximizing the utilization of data on efficacy and safety. On this note, there is enthusiasm for implementing some less popular but efficient alternative designs for confirmatory pediatric trials. Within the pediatric extrapolation paradigm, examples of such designs, other than purely based on pharmacokinetic/pharmacodynamic data, are described in this article along with their advantages and disadvantages. This article will also discuss how to incorporate alternative data sources in the analysis of pediatric clinical trials. A discussion of existing approaches and a road-map to their utilization will be provided. Real case examples on the use of the approaches are provided.     

Extending multilevel spatial models to include spatially varying coefficients

Multilevel models have long been used by health geographers working on questions of space, place, and health. Similarly, health geographers have pursued interests in determining whether or not the effect of an exposure on a health outcome varies spatially. However, relatively little work has sought to use multilevel models to explore spatial variability in the effects of a contextual exposure on a health outcome. Methodologically, extending multilevel models to allow intercepts and slopes to vary spatially is straightforward. The purpose of this paper, therefore, is to show how multilevel spatial models can be extended to include spatially varying covariate effects. We provide an empirical example on the effect of agriculture on malaria risk in children under 5 years of age in the Democratic Republic of Congo.     

Obstetric Violence as Reproductive Governance in the Dominican Republic

ABSTRACT

A human rights violation, obstetric violence encompasses numerous forms of mistreatment against women giving birth in health care facilities. Based on this framework, we conducted open-ended exit interviews with 43 women who had given birth at either one of the two largest public maternity hospitals in the Dominican Republic. Women’s narratives revealed a contrast between scholarly definitions of obstetric violence and their own perceptions of receiving abusive care. Analyzing obstetric violence as a form of reproductive governance and the adaptive preference that ensues helps explain why most women accepted with endurance the poor quality of care that they received.     

Prevalence and correlates of everyday discrimination among black Caribbeans in the United States: the impact of nativity and country of origin

Objectives: Black Caribbeans in the United States have been the victims of major discrimination (e.g. unfairly fired, denied a promotion, denied housing). What is not known is the degree to which they also experience more routine forms of everyday discrimination such as receiving poor restaurant service, being perceived as dishonest, and being followed in stores. This paper investigates the distribution and correlates of everyday discrimination among a national sample of black Caribbeans in the U.S.

Design: This analysis used the black Caribbean sub-sample (n?=?1,621) of the National Survey of American Life. Demographic and immigration status correlates of ten items from the Everyday Discrimination Scale were investigated: being treated with less courtesy, treated with less respect, receiving poor restaurant service, being perceived as not smart, being perceived as dishonest, being perceived as not as good as others, and being feared, insulted, harassed, or followed in stores.

Results: Roughly one out of ten black Caribbeans reported that, on a weekly basis, they were treated with less courtesy and other people acted as if they were better than them, were afraid of them, and as if they were not as smart. Everyday discrimination was more frequent for black Caribbeans who were male, never married, divorced/separated, earned higher incomes, and who were second or third generation immigrants. Black Caribbeans attributed the majority of the discrimination they experienced to their race.

Conclusion: To our knowledge, this is the first study to provide an in-depth investigation of everyday discrimination among the black Caribbean population. It provides the frequency, types and correlates of everyday discrimination reported by black Caribbeans in the United States. Understanding the frequency and types of discrimination is important because of the documented negative impacts of everyday discrimination on physical and mental health.     

Socioeconomic deprivation is inversely associated with measles incidence: a longitudinal small-area analysis,Germany, 2001 to 2017

BackgroundAlthough measles is endemic throughout the World Health Organization European Region, few studies have analysed socioeconomic inequalities and spatiotemporal variations in the disease’s incidence.AimTo study the association between socioeconomic deprivation and measles incidence in Germany, while considering relevant demographic, spatial and temporal factors.MethodsWe conducted a longitudinal small-area analysis using nationally representative linked data in 401 districts (2001–2017). We used spatiotemporal Bayesian regression models to assess the potential effect of area deprivation on measles incidence, adjusted for demographic and geographical factors, as well as spatial and temporal effects. We estimated risk ratios (RR) for deprivation quintiles (Q1–Q5), and district-specific adjusted relative risks (ARR) to assess the area-level risk profile of measles in Germany.ResultsThe risk of measles incidence in areas with lowest deprivation quintile (Q1) was 1.58 times higher (95% credible interval (CrI): 1.32–2.00) than in those with highest deprivation (Q5). Areas with medium-low (Q2), medium (Q3) and medium-high deprivation (Q4) had higher adjusted risks of measles relative to areas with highest deprivation (Q5) (RR: 1.23, 95%CrI: 0.99–1.51; 1.05, 95%CrI: 0.87–1.26 and 1.23, 95%CrI: 1.05–1.43, respectively). We identified 54 districts at medium-high risk for measles (ARR > 2) in Germany, of which 22 were at high risk (ARR > 3).ConclusionSocioeconomic deprivation in Germany, one of Europe’s most populated countries, is inversely associated with measles incidence. This association persists after demographic and spatiotemporal factors are considered. The social, spatial and temporal patterns of elevated risk require targeted public health action and policy to address the complexity underlying measles epidemiology.     

单臂试验连续型数据的贝叶斯Meta分析方法及实现

目的 介绍单臂试验连续型数据的Meta分析模型、贝叶斯方法及实现。方法 阐述正态-正态层次模型,基于该模型框架,以贝叶斯方法拟合随机效应模型,对效应参数μ和异质性参数τ分别选择不同的先验,使用R软件的bayesmeta包对两个文献数据重新分析。结果 在正态-正态层次模型框架下,基于不同的先验信息,贝叶斯Meta分析结果为:数据1参数μ的点估计及95%CI分别为-4.26(-6.97, -1.92)和-4.50(-9.27, -0.53),参数τ点估计及95%CI分别为1.51(0.41, 2.75)和2.28(0.00, 6.57);数据2参数μ的点估计及95%CI分别为-4.07(-5.54, -2.71)和-4.12(-5.96,-2.46),参数τ点估计及95%CI分别为1.54(0.78, 2.48)和1.81(0.74, 3.51)。结论 不同的先验可能影响参数估计值。基于NNHM框架下的贝叶斯方法适用于单臂试验连续型数据的Meta分析。Bayesmeta包以其简单、快速、准确、可重量性算法等可以用于实现贝叶斯随机效应模型Meta分析。