Semiparametric Bayesian analysis of structural equation models with fixed covariates

Latent variables play the most important role in structural equation modeling. In almost all existing structural equation models (SEMs), it is assumed that the distribution of the latent variables is normal. As this assumption is likely to be violated in many biomedical researches, a semiparametric Bayesian approach for relaxing it is developed in this paper. In the context of SEMs with covariates, we provide a general Bayesian framework in which a semiparametric hierarchical modeling with an approximate truncation Dirichlet process prior distribution is specified for the latent variables. The stick-breaking prior and the blocked Gibbs sampler are used for efficient simulation in the posterior analysis. The developed methodology is applied to a study of kidney disease in diabetes patients. A simulation study is conducted to reveal the empirical performance of the proposed approach. Supplementary electronic material for this paper is available in Wiley InterScience at http://www.mrw.interscience.wiley.com/suppmat/1097-0258/suppmat/.     

Bayesian semiparametric analysis of structural equation models with mixed continuous and unordered categorical variables

Recently, structural equation models (SEMs) have been applied for analyzing interrelationships among observed and latent variables in biological and medical research. Latent variables in these models are typically assumed to have a normal distribution. This article considers a Bayesian semparametric SEM with covariates, and mixed continuous and unordered categorical variables, in which the explanatory latent variables in the structural equation are modeled via an appropriate truncated Dirichlet process with a stick‐breaking procedure. Results obtained from a simulation study and an analysis of a real medical data set are presented to illustrate the methodology. Copyright © 2009 John Wiley & Sons, Ltd.     

Non-normal path analysis in the presence of measurement error and missing data: a Bayesian analysis of nursing homes' structure and outcomes

Path analytic models are useful tools in quantitative nursing research. They allow researchers to hypothesize causal inferential paths and test the significance of these paths both directly and indirectly through a mediating variable. A standard statistical method in the path analysis literature is to treat the variables as having a normal distribution and to estimate paths using several least squares regression equations. The parameters corresponding to the direct paths have point and interval estimates based on normal distribution theory. Indirect paths are a product of the direct path from the independent variable to the mediating variable and the direct path of the mediating variable to the dependent variable. However, in the case of non-normal distributions, the point and interval estimates of the indirect path become much more difficult to estimate. We address the issue of calculating indirect point and interval estimates in the case of non-normally distributed data. Our substantive application is a nursing home research problem in which the variables in the path analysis of interest involve variables with normal, Bernoulli, or Poisson distributions. Additionally, one of the Poisson variables is observed with error. This paper addresses estimating point and interval estimation of indirect paths for variables with non-normal distributions in the presence of missing data and measurement error. We handle these difficulties from a fully Bayesian point of view. We present our substantive path analysis motivated from a nursing home structure, process, and outcomes model. Our results focus on the impact job turnover in the nursing homes has on nursing home outcomes.     

Multilevel modelling of clustered grouped survival data using Cox regression model: an application to ART dental restorations

In some controlled clinical trials in dental research, multiple failure time data from the same patient are frequently observed that result in clustered multiple failure time. Moreover, the treatments are often delivered by more than one operator and thus the multiple failure times are clustered according to a multilevel structure when the operator effects are assumed to be random. In practice, it is often too expensive or even impossible to monitor the study subjects continuously, but they are examined periodically at some regular pre-scheduled visits. Hence, discrete or grouped clustered failure time data are collected. The aim of this paper is to illustrate the use of the Monte Carlo Markov chain (MCMC) approach and non-informative prior in a Bayesian framework to mimic the maximum likelihood (ML) estimation in a frequentist approach in multilevel modelling of clustered grouped survival data. A three-level model with additive variance components model for the random effects is considered in this paper. Both the grouped proportional hazards model and the dynamic logistic regression model are used. The approximate intra-cluster correlation of the log failure times can be estimated when the grouped proportional hazards model is used. The statistical package WinBUGS is adopted to estimate the parameter of interest based on the MCMC method. The models and method are applied to a data set obtained from a prospective clinical study on a cohort of Chinese school children that atraumatic restorative treatment (ART) restorations were placed on permanent teeth with carious lesions. Altogether 284 ART restorations were placed by five dentists and clinical status of the ART restorations was evaluated annually for 6 years after placement, thus clustered grouped failure times of the restorations were recorded. Results based on the grouped proportional hazards model revealed that clustering effect among the log failure times of the different restorations from the same child was fairly strong (corr(child)=0.55) but the effects attributed to the dentists could be regarded as negligible (corr(dentist)=0.03). Gender and the location of the restoration were found to have no effects on the failure times and no difference in failure times was found between small restorations placed on molars and non-molars. Large restorations placed on molars were found to have shorter failure times compared to small restorations. The estimates of the baseline parameters were increasing indicating increasing hazard rates from interval 1 to 6. Results based on the logistic regression models were similar. In conclusion, the use of the MCMC approach and non-informative prior in a Bayesian framework to mimic the ML estimation in a frequentist approach in multilevel modelling of clustered grouped survival data can be easily applied with the use of the software WinBUGS.     

齐齐哈尔市儿童替牙期及牙齿健康状况的横断面调查

目的 了解儿童替牙期现状、常见牙齿问题及影响因素、家长对儿童牙齿健康知识的知晓情况,为科学维护儿童口腔健康提供依据。方法 2019年1-5月本研究采用横断面调查的方法,通过分层整群抽样的方法以齐齐哈尔市的1 023名4～7岁儿童为研究对象,探究其替牙期常见的牙齿健康问题及影响因素。结果 本次调查发现,有435名(42.52%)儿童进入了替牙期;185名(18.10%)儿童牙齿受过外伤。875名儿童接受口腔检查,266名(30.4%)儿童患有龋齿。通过Logistic回归分析,发现摄入果汁等饮料的频率≥3次/d(OR=2.254,95%CI:1.185～4.289,P＜0.05)、经常睡前吃零食(OR=2.515,95%CI:1.437～4.403,P＜0.05)、进食后未进行口腔护理(OR=2.490,95%CI:1.412～4.390,P＜0.05)为龋齿的危险因素。进食糖果≥3次/d(OR=3.924,95%CI:1.264～12.184,P＜0.05)及进食甜点1~2次/d(OR=3.378,95%CI:1.219～9.361,P＜0.05)是儿童提前进入替牙期的危险因素。结论 部分儿童存在替牙期提前的状况,家庭因素、饮食习惯和生活方式是影响儿童牙齿健康的因素,应提高家长对儿童口腔健康的认识,科学合理的维护儿童口腔健康。     

中国成年及老年人预防性口腔卫生行为状况

目的 了解中国35~44岁成年人和65~74岁老年人看牙、洗牙、刷牙、使用含氟牙膏4种预防性口腔行为水平及达到《中国居民口腔健康指南》推荐标准情况。方法 采用多阶段分层随机整群抽样方法,对全国23 271名35~44岁成年人和8 902名65~74岁老年人就上述4种口腔卫生行为进行问卷调查,采用描述性统计分析方法对调查结果进行分析。结果 中国35~44岁居民达到《指南》推荐的“每年进行1次口腔检查”、“每年洗牙1次”、“早晚刷牙”、“使用含氟牙膏刷牙” 4种口腔卫生行为的人数比例分别为8.4%、2.3%、36.3%和42.3%,65~74岁居民达到标准的人数比例分别为12.1%、1.5%、22.1%和32.5%;城市居民4种口腔行为达到推荐标准的人数比例均高于农村居民(P<0.05);每年口腔检查、早晚刷牙、使用含氟牙膏行为达标比例均为东部地区>中部地区>西部地区(P<0.05);女性口腔检查和早晚刷牙情况好于男性(P<0.05);不同地区、性别居民洗牙情况差异无统计学意义(P>0.05)。结论 当前中国成年人和老年人的4种口腔卫生行为达到《指南》推荐标准的人群比例均较低,并以农村地区、西部地区、男性最为突出,应采取及时有效的干预措施。     

A Bayesian analysis of bivariate ordinal data: Wisconsin epidemiologic study of diabetic retinopathy revisited

In many biomedical experiments one may often encounter bivariate data which are component-wise ordinal. The data set of the ophthalmologic experiment of the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) is an example of such data. Several authors considered the analysis of such data from different viewpoints. The present work reviews the existing literature based on the WESDR data and on the basis of some latent variables provide the technique for analysing such data more easily in a Bayesian framework. Computation supports the methodology to a great extent. A comparison between our approach and the likelihood based approach considered by Kim has also been made.     

Analysis of mixed categorical data in 2 x K contingency tables

2 x K contingency tables having both ordinal and nominal categories are often encountered in various types of studies. Such data are referred to as 'mixed' categorical data in this article. To apply a method for ordered categorical data one has to discard the nominal categories, and to apply a method for nominal categories one has to discard the ordering information inherent in the ordered categories. Therefore, investigators often either discard observations in nominal categories or discard the ordering of the categories before analysing such data. Some information will be lost in both approaches. A method for analysing data in 2 x K 'mixed' tables is proposed in this paper which can be considered as an extension of well known methods for nominal and ordered categories. The proposed method utilizes observations in the nominal categories as well as the ordering information. If all the categories were ordered then the proposed method reduces to the trend test, and if all the categories were nominal then the proposed method reduced to Pearson's chi-square test.     

Bayesian modeling of multivariate average bioequivalence

Bioequivalence trials are usually conducted to compare two or more formulations of a drug. Simultaneous assessment of bioequivalence on multiple endpoints is called multivariate bioequivalence. Despite the fact that some tests for multivariate bioequivalence are suggested, current practice usually involves univariate bioequivalence assessments ignoring the correlations between the endpoints such as AUC and C(max). In this paper we develop a semiparametric Bayesian test for bioequivalence under multiple endpoints. Specifically, we show how the correlation between the endpoints can be incorporated in the analysis and how this correlation affects the inference. Resulting estimates and posterior probabilities 'borrow strength' from one another where the amount and the direction of the strength borrowed are determined by the prior correlations. The method developed is illustrated using a real data set.     

吉林省儿童口腔健康素养及其保健行为现状分析

目的 了解吉林省儿童的口腔健康素养的掌握情况与行为的实施情况,为制定口腔保健干预提供科学依据。方法 整群抽取吉林省内8所学校一至五年级9 391名小学生,采用自行设计问卷进行调查。结果 口腔健康素养调查结果显示,在口腔保健知识中,使用含氟牙膏、更换牙刷时间和睡前刷牙的正确回答率分别为46.7%、70.3%和85.5%;口腔保健技能中,牙齿缝隙刷洗、牙齿表面刷洗及刷牙时间各为68.5%、64.7%和75.2%;口腔保健行为中,早晚都刷牙、睡前不吃东西和饭后漱口分别为77.5%、93.7%和84.6%。结论 吉林省儿童的口腔健康素养偏低,口腔保健行为有待于提高。学校、社区和家庭共同推动儿童健康促进,提高儿童口腔健康素养和保健行为,促进儿童口腔健康。     

健康教育对儿童龋齿患病率的影响

目的：探究健康教育对儿童龋齿患病率的影响。方法随机抽取2010年3月至2012年3月浙江省玉环县两所人数和教育水平相当的幼儿园中的儿童共1506例作为研究对象。 A幼儿园为实验组,连续2年施行每年1次氯化泡沫治疗、发放宣传手册和进行口腔保健知识教育等干预措施,男童434人,女童389人;B幼儿园为对照组,未施行任何干预措施,男童354人,女童329人。通过问卷调查的方式对儿童龋齿患病情况进行研究,得出健康教育对儿童龋齿患病情况的影响。结果实验组的龋齿患病率为35.50%,低于对照组的45.45%(χ2=5.71,P<0.05);对照组3岁、4岁及6岁的龋齿患病率分别为61.9%、55.9%和60.9%,实验组3岁、4岁及6岁的龋齿患病率分别为38.1%、44.1%、和39.1%,两组儿童各年龄段龋齿患病率差异均有统计学意义(χ2值分别为4.253、3.345及5.651,均P<0.05)。结论实施安全教育的地区儿童的龋齿患病发生率明显低于未施行地区,健康教育可显著降低儿童龋齿患病率。     

张家港市学龄前儿童患龋情况及口腔健康行为分析

目的 了解张家港市学龄前儿童患龋情况及口腔健康行为,为龋病防治提供依据。方法 采取分层、随机、整群抽样的方法,选择张家港市16所幼儿园的1 405名学龄前儿童,对其进行口腔检查并发放问卷,由家长填写后统一收回。结果 学龄前儿童患龋率为 61.99%,龋均为2.81,龋失补充填率为3.54%;男、女童患龋率分别为63.21%、60.63%,差异无统计学意义(P＞0.05),城、乡患龋率分别为60.86%、63.36%,差异无统计学意义(P＞0.05);随着年龄的增长,患龋率升高,在不同年龄组间患龋率差异有统计学意义(χ²=39.470,P＜0.05);3岁以前开始刷牙的儿童占30.25%,从长第一个牙就开始刷牙的仅占1.00%, 18.78%的儿童只是偶尔刷牙甚至不刷牙。饮食方面仅有23.42%的儿童不在睡前吃甜食。睡前高频进食甜食和开始刷牙的年龄较大是儿童龋病发生的重要危险因素,结果均有统计学意义(χ²=9.740、14.913,P＜0.05)。结论 张家港市学龄前儿童的患龋率较高,口腔健康行为较差,应加强对儿童和家长的口腔健康教育。     

苏州市牙科手机清洗质量横断面调查分析

目的了解苏州市各级各类医疗机构牙科手机的清洗现状和清洗质量。方法采用横断面调查的方法,按照等比例系统抽样原则,2015年10月26—31日对全市范围内口腔诊疗机构进行抽样检查,采用ATP生物荧光检测法检测各单位手机清洗质量。结果在全市10个行政区范围内共抽检医疗机构72所,检测手机201支,样本402份,其中有42份不合格,不合格率为10.45%。手机表面清洗不合格率为17.91%,高于手机水路的不合格率(2.99%),差异有统计学意义(P0.05)。不同等级医疗机构清洗质量比较,差异有统计学意义(P0.05),三级医院全部合格,无等级医疗机构手机清洗不合格率达14.45%。按不同医疗机构名称分类,清洗质量比较差异有统计学意义(P0.05),公立医院口腔科的手机清洗效果最好(不合格率4.31%),私营口腔诊所的清洗效果最差(不合格率13.81%)。结论应加强对全市牙科手机清洗的教育培训,尤其是低等级、私营口腔诊所牙科手机清洗质量的监督管理。     

Joint modeling of longitudinal data and informative dropout time in the presence of multiple changepoints

In longitudinal studies of patients with the human immunodeficiency virus (HIV), objectives of interest often include modeling of individual-level trajectories of HIV ribonucleic acid (RNA) as a function of time. Such models can be used to predict the effects of different treatment regimens or to classify subjects into subgroups with similar trajectories. Empirical evidence, however, suggests that individual trajectories often possess multiple points of rapid change, which may vary from subject to subject. Additionally, some individuals may end up dropping out of the study and the tendency to drop out may be related to the level of the biomarker. Modeling of individual viral RNA profiles is challenging in the presence of these changes, and currently available methods do not address all the issues such as multiple changes, informative dropout, clustering, etc. in a single model. In this article, we propose a new joint model, where a multiple-changepoint model is proposed for the longitudinal viral RNA response and a proportional hazards model for the time of dropout process. Dirichlet process (DP) priors are used to model the distribution of the individual random effects and error distribution. In addition to robustifying the model against possible misspecifications, the DP leads to a natural clustering of subjects with similar trajectories which can be of importance in itself. Sharing of information among subjects with similar trajectories also results in improved parameter estimation. A fully Bayesian approach for model fitting and prediction is implemented using MCMC procedures on the ACTG 398 clinical trial data. The proposed model is seen to give rise to improved estimates of individual trajectories when compared with a parametric approach.     

HGLM versus conditional estimators for the analysis of clustered binary data

Clustered binary data arise frequently in medical research such as cross-over clinical trials and twin studies. For the analysis of such data either a random-effects model or a conditional likelihood approach can be used. In this paper, we compare numerically the random-effects model estimator and the conditional likelihood estimator and discuss their relative merits for the analysis of binary data.     

中国母乳喂养与学龄儿童乳牙龋齿关系meta分析

目的 综合评价母乳喂养对学龄儿童龋齿的影响作用,为龋齿早期预防提供科学依据。方法 系统检索中国生物医学文献数据库(CBM)、中国知网(CNKI)、维普(VIP)和万方数据库中1996-2015年发表的有关喂养方式与儿童龋齿文章,采用Stata 11.0软件进行meta分析。结果 共纳入符合标准文献20篇,17篇为横断面研究,3篇为病例对照研究,样本总量为18 707例;与非母乳喂养组比较,母乳喂养降低儿童龋齿的发病风险(OR=0.581,95%CI=0.433~0.779,P<0.001);亚组分析结果没有明显降低异质性,并且显示只有在2005年之前发表、非核心期刊、横断面研究、汉族、龋齿未参考WHO诊断标准、北方地区人群的研究中,母乳喂养可降低儿童龋齿的患病率;敏感性分析结果显示,所有纳入文献稳定性较好;所纳入文献不存在发表偏倚。结论 母乳喂养对学龄儿童龋齿发生有明显保护作用,因此要提倡母乳喂养。     

中国卫生资源配置效率DEA和SFA组合分析

目的 应用科学、客观和较全面的评价方法对中国卫生资源配置效率进行研究,为卫生事业管理科学化提供依据。方法 采用数据包络分析和随机前沿分析,分别对2004-2013年间中国卫生资源配置的技术效率和成本效率进行评价分析,找出影响2种效率的因素。结果 2004-2013年,中国卫生资源配置技术效率不断提高,规模报酬趋于稳定,平均技术效率值为0.997,技术有效率为50%;中国卫生资源配置成本效率差异较大,平均成本效率值仅为0.690,有4个自变量（卫生人员数、诊疗人次、入院人数和平均住院日）对总成本具有负影响。结论 政府相关部门应当重视效率测算,合理解决中国卫生资源配置中存在的问题,加强科学化管理。