基于潜变量得分的多水平多变量回归模型在职业紧张评价中的应用

目的介绍基于潜变量得分的多水平多反应变量回归模型在职业紧张评价中的应用。方法为克服测量误差的存在，以职业紧张量表14个分项的潜变量得分，将之作为中问结果引入多水平多反应变量回归模型。结果职业任务各分项不同程度地引起职业紧张，而个体应变能力是减轻职业紧张行之有效的方式。随机系数反映这些影响在不同科室存在着不同。结论采用基于潜变量得分的多水平多反应变量回归模型既可有效降低测量误差，又得以合理地解释。尤其对于系统结构数据，多元线性模型的多水平理论比多水平潜变量分析方法更成熟可信。     

多反应变量模型在临床实例中的应用研究

[目的]将多反应变量模型应用到临床实践中.[方法]在MLwiN2.0软件中运用临床实例阐述多反应变量模型的构造、参数估计及结果的解释.[结果]在最后所拟合的二元两水平模型中,得到两个反应变量之闻的相关系数;在考虑了结果变量之间的相关性后,能获得参数的有效估计,不仅可以比较不同影响因素对同一个反应变量的影响效应大小,还可以比较同一个影响园素对两个不同反应变量的影响效应大小.[结论]多反应变量模型适合于临床资料中多个相关反应变量的回归分析.     

双变量横断面资料的多水平模型分析

目的 分析双变量横断面资料两个反应变量之间的相关性随协变量变化的规律。方法 双变量多水平模型,最小二乘法。结果 双变量多水平模型可以估计各水平两个变量的方差－协方差阵,据此可以计算出相关系数随协变量变化的函数式。结论 双变量多水平模型是分析双变量横断面资料相关性的有力工具,克服了用简单直线相关处理这类资料的局限性。     

多元多水平模型在儿童生长发育研究中的应用

数据的层次结构(hierarchical or cluster structure，或系统结构)现象广泛存在于医学的各个领域。如生长发育的调查中，按地区、人群进行分层抽样，所得的数据有地区和个人2个层次；同时，研究者感兴趣的反应变量或结局变量常不止1个。许多健康状态的测量经常是多维的。由于同一个体的不同测量指标之间可能存在一定相关性。当对反应变量之间的相关作为一种信息拟合多反应变量的模型。获得它们之间相关程度有效估计时，传统的多元回归方法不再适宜。本文采用儿童生长发育研究实例阐述多元多水平模型的构造及估计实现，为同类研究提供方法参考。     

多元多水平模型及其在儿童生长发育研究中的应用

目的传统的统计方法不宜用于分析具有层次结构特征的多反应变量数据,本文探讨了多元多水平模型的原理及应用.方法利用儿童生长发育研究的实例介绍多元多水平模型的构造及其应用.结果获得了解释变量系数的有效估计及身长和体重在各个水平上关系.结论多水平模型可以灵活有效地处理各种具有层次结构的数据.     

多水平logistic模型在问卷信度研究中的应用

目的：探讨离散型反应变量多水平统计模型在问卷信度研究中的应用价值。方法：采用多水平ｌｏｇｉｓｔｉｃ模型对问卷中的三个问题的信度分别进行了分析。结果：问卷中三个问题均存在不同程度的“调查员变异”，且调查员与调查对象特征与问题应答具有联系。结论：在不需要重复调查的情况下，多水平分析技术不仅可评价问卷项目应答的信度，而且可评价调查员和调查对象特征对调查结果的影响     

多分类有序反应变量logistic回归应用条件的检验

目的 对多分类有序反应变量logistic回归的应用条件寻求科学合理的检验方法。方法 使用卡方分布的理论，SAS软件及抽样调查方法。结果 设计出多分类有序反应变量logistic回归应用条件的卡方检验方法，推导出反应变量取各水平的概率计算公式及卡方检验中理论值、自由度的计算公式，并在作者主持的国家医师资格临床实践技能考试研究中取得了成功效果。结论 多分类有序反应变量logistic回归得到完善和补充，具有较大的理论和实际意义。     

二分类反应变量多水平模型及其软件实现

目的本文针对反应变量是二分类、且存在层次结构的数据,给出多水平模型的建模步骤及在软件SPSS、SAS中的实现方法指引,为医学研究者处理这类数据提供参考。方法以卫生服务调查数据为例说明二分类反应变量两水平logistic回归模型的拟合步骤及软件实现。结果如果两水平零模型的随机系数σ检验具有统计学意义,则有必要拟合多水平模型。结论结合专业知识或对σ进行检验判断有无必要考虑高水平效应。SPSS中实现要求19.0及以上版本,操作相对简单,适合初步使用者;SAS中实现建议使用Nlmixed过程语句,利用Glimmix过程语句拟合模型的参数估计结果,为其设定相应参数初始值提供依据。     

一种空间负二项回归模型及其应用

目的研究用于具有空间相关性资料的一种新的统计分析方法:空间负二项回归。方法构建空间负二项回归模型,利用蒙特卡罗最大似然法进行模型参数估计,并采用钉螺实例,比较常规负二项回归与空间负二项回归的模型拟合与参数估计效果。结果实例分析表明,当资料存在空间相关性时,空间负二项回归可以有效地提高数据拟合精度,改善参数估计。结论空间负二项回归是一种可以用于分析具有空间相关性计数型反应变量的广义线性模型。     

子宫肌瘤患者治疗前后多项血清因子水平变化规律研究

[目的]探讨子宫肌瘤患者治疗前后多项血清因子水平的变化规律。[方法]选取2009年6月~2010年10月于本院进行治疗的58例子宫肌瘤患者为观察组,同时选取58名健康体检人员为对照组,后将观察组治疗前及治疗后12 h、1 d及3 d的血清EGF、IL-6、IL-8、CRP、Tf、MDA及SOD水平进行统计及比较。[结果]经研究比较发现,随着时间推移观察组血清EGF、Tf、MDA呈现下降趋势,血清IL-6、IL-8、CRP水平呈现先升后降的趋势,血清SOD水平呈现升高趋势,逐渐接近对照组水平。[结论]子宫肌瘤患者治疗前后多项血清因子水平变化规律较为明显,值得进一步研究及探讨。     

Mixed multivariate generalized linear models for assessing lower-limb arterial stenoses

Experiments and observational studies often involve gathering information on several response variables, enabling us to model their dependence on observable predictor variables. Despite associations between the response variables, they are often analysed separately using general and generalized linear models. This paper investigates applications of multivariate regression analysis to improve the accuracy of predictions and decisions, in the specific context of diagnosing arterial stenoses in human legs. Two basic models are developed for this application, using (i) four binary responses and (ii) a mixture of two binary and two normal responses. The results clearly demonstrate the potential advantages offered by this approach.     

肝炎中医辨证分型研究中潜在类别模型的应用

目的对肝炎的中医症状、体征分类,探讨潜在类别模型在多个二分类反应变量聚类分析中的应用。方法本研究共收集2011年全国56家医院接受中医治疗或者中西医结合治疗的肝炎患者信息,建立肝炎患者中医症状和体征的数据库,最终得到737例肝炎患者症状和体征信息。利用潜在类别模型对肝炎患者的中医证候进行辨证(SAS软件Proc LCA模块),以探讨显在变量(中医症状)和潜在变量(中医证候)之间的内在联系,利用最少的潜在分类来解释显变量的关联程度。潜类别分析过程包括模型参数化、参数估计、模型识别、模型评价、潜在分类和结果解释。结果以BIC最小为标准,5个潜类别的潜变量模型是数据拟合的理想模型(BIC=2 601.43)。依据中医证候(中医症状、体征),对各种潜在类别依据其下各项目的条件概率特点进行潜类别解释,按照后验概率,737例肝炎患者可以分为5类,分别为:肝肾阴虚,湿阳脾胃,湿热中阻,肝郁脾虚,肝郁气滞。各组的人数分别为:148人,170人,46人,265人,108人。结论潜在类别分析是描述一组分类变量间相互关系所形成的数学模型,综合了结构方程模型与对数线性模型的思想,潜变量的"降维简化"技术既可以从群体角度帮助我们确定肝炎的证候分类,也可以从个体角度确定其属的证候,达到中医辨证的目的。     

Genetic Association With Multiple Traits in the Presence of Population Stratification

Testing association between a genetic marker and multiple‐dependent traits is a challenging task when both binary and quantitative traits are involved. The inverted regression model is a convenient method, in which the traits are treated as predictors although the genetic marker is an ordinal response. It is known that population stratification (PS) often affects population‐based association studies. However, how it would affect the inverted regression for pleiotropic association, especially with the mixed types of traits (binary and quantitative), is not examined and the performance of existing methods to correct for PS using the inverted regression analysis is unknown. In this paper, we focus on the methods based on genomic control and principal component analysis, and investigate type I error of pleiotropic association using the inverted regression model in the presence of PS with allele frequencies and the distributions (or disease prevalences) of multiple traits varying across the subpopulations. We focus on common alleles but simulation results for a rare variant are also reported. An application to the HapMap data is used for illustration.     

A test of interaction between matching variables and risk factors in 2 matched samples

The paired two sample problem with a binary response is considered in order to investigate interactions between the sampling factor and matching variables. A simple test for those interactions is described.     

二分类 Logistic 回归在冠心病危险因素研究中的应用

目的：应用二分类Logistic回归在可能引发冠心病影响因素中筛选危险因素，建立冠心病危险因素“最优”回归方程。方法采取系统抽样方法，对某几所医院心血管内科初诊为冠心病并进行冠脉造影病例，抽取400例30～65岁患者病例作为样本。通过二分类Logistic回归方法分析冠心病与危险因素的相关关系。结果以是否冠心病为因变量，各因素为自变量，筛选影响因素。经相关分析、共线性诊断，筛选出冠心病危险因素为年龄、合并疾病、吸烟、收缩压、血糖、尿酸、低密度脂蛋白、脂蛋白（ a）。结论用二分类Logistic回归找出危险因素，可以有效分析各因素的相对重要性。     

多重应答资料统计分析及其SAS软件实现过程

目的 探讨现场调查中多重应答资料的统计推断方法.方法 根据多重应答资料不同选项间既存在关联,又不可互换的特点,将其视为多因变量的多元logistic回归问题,采用广义估计方程建模分析,并通过实例及其SAS编程的实现过程进行阐述.结果 广义估计方程可以很好地分析自变量对于多重应答资料各项的影响,并能进一步回答各选项的基线应答概率、作业相关矩阵信息;在此模型下,还可以进一步分析各影响因素对于每一选项的具体影响,或各自变量的分层比较具体差异的信息.结论 广义估计方程可以作为现场调查中多重应答资料的推断分析.     

以广义估计方程研究浙江省肺结核耐药预测方程

目的 耐药肺结核患者可能对一种或多种抗结核药物耐药。对这类因变量为多结局非独立的数据,本文探讨应用广义估计方程分析耐药危险因素,构建预测方程,探索预警模型建立方向。方法 对浙江省30个耐药监测点的涂阳患者进行药敏检测和问卷调查,以对13种抗结核药物的耐药情况为因变量,可能危险因素为自变量,用SAS的GENMOD模块构建广义估计模型。结果 本研究中基线水平下发生耐药的概率为20.26%,有统计学意义的耐药影响因素包括年龄、保险、是否合并乙型肝炎、治疗史及停药情况。根据各因素对耐药发生的影响程度得到预测方程。结论 广义估计方程解决了耐药数据因变量相关性的问题,有效利用非独立数据提供的信息,且参数估计稳健,为耐药危险因素评价和预警模型构建提供更全面的信息。     

Estimation of causal mediation effects for a dichotomous outcome in multiple‐mediator models using the mediation formula

Mediators are intermediate variables in the causal pathway between an exposure and an outcome. Mediation analysis investigates the extent to which exposure effects occur through these variables, thus revealing causal mechanisms. In this paper, we consider the estimation of the mediation effect when the outcome is binary and multiple mediators of different types exist. We give a precise definition of the total mediation effect as well as decomposed mediation effects through individual or sets of mediators using the potential outcomes framework. We formulate a model of joint distribution (probit‐normal) using continuous latent variables for any binary mediators to account for correlations among multiple mediators. A mediation formula approach is proposed to estimate the total mediation effect and decomposed mediation effects based on this parametric model. Estimation of mediation effects through individual or subsets of mediators requires an assumption involving the joint distribution of multiple counterfactuals. We conduct a simulation study that demonstrates low bias of mediation effect estimators for two‐mediator models with various combinations of mediator types. The results also show that the power to detect a nonzero total mediation effect increases as the correlation coefficient between two mediators increases, whereas power for individual mediation effects reaches a maximum when the mediators are uncorrelated. We illustrate our approach by applying it to a retrospective cohort study of dental caries in adolescents with low and high socioeconomic status. Sensitivity analysis is performed to assess the robustness of conclusions regarding mediation effects when the assumption of no unmeasured mediator‐outcome confounders is violated. Copyright © 2013 John Wiley & Sons, Ltd.     

Recovery of information from multiple imputation: a simulation study

ABSTRACT: BACKGROUND: Multiple imputation is becoming increasingly popular for handling missing data. However, it is often implemented without adequate consideration of whether it offers any advantage over complete case analysis for the research question of interest, or whether potential gains may be offset by bias from a poorly fitting imputation model, particularly as the amount of missing data increases. METHODS: Simulated datasets (n = 1000) drawn from a synthetic population were used to explore information recovery from multiple imputation in estimating the coefficient of a binary exposure variable when various proportions of data (10-90%) were set missing at random in a highly-skewed continuous covariate or in the binary exposure. Imputation was performed using multivariate normal imputation (MVNI), with a simple or zero-skewness log transformation to manage non-normality. Bias, precision, mean-squared error and coverage for a set of regression parameter estimates were compared between multiple imputation and complete case analyses. RESULTS: For missingness in the continuous covariate, multiple imputation produced less bias and greater precision for the effect of the binary exposure variable, compared with complete case analysis, with larger gains in precision with more missing data. However, even with only moderate missingness, large bias and substantial under-coverage were apparent in estimating the continuous covariate's effect when skewness was not adequately addressed. For missingness in the binary covariate, all estimates had negligible bias but gains in precision from multiple imputation were minimal, particularly for the coefficient of the binary exposure. CONCLUSIONS: Although multiple imputation can be useful if covariates required for confounding adjustment are missing, benefits are likely to be minimal when data are missing in the exposure variable of interest. Furthermore, when there are large amounts of missingness, multiple imputation can become unreliable and introduce bias not present in a complete case analysis if the imputation model is not appropriate. Epidemiologists dealing with missing data should keep in mind the potential limitations as well as the potential benefits of multiple imputation. Further work is needed to provide clearer guidelines on effective application of this method.