多水平分析在调查问卷结果分析中的应用北大核心CSCD

目的探讨应用两水平分层logistic回归模型分析调查问卷二分类结局变量资料的影响因素,对影响因素做出正确的评价和解释。方法以学生的调查问卷作为分析对象,运用两水平分层logistic回归模型,拟合一系列零模型、随机截距模型,识别学生水平和学校水平因素的影响大小。结果学生对所研究问题的看法受到学生个体特征和学校特征的影响,学生层面中学生成绩影响较大。结论多水平分析方法是处理分层嵌套数据的有用工具,利用两水平分层logistic回归模型可以同时探讨学生层面和学校层面解释变量对结局变量的效应,对于分析嵌套数据结构资料结局变量的影响因素有优越性。     

多水平分析在调查问卷结果分析中的应用

目的探讨应用两水平分层logistic回归模型分析调查问卷二分类结局变量资料的影响因素,对影响因素做出正确的评价和解释。方法以学生的调查问卷作为分析对象,运用两水平分层logistic回归模型,拟合一系列零模型、随机截距模型,识别学生水平和学校水平因素的影响大小。结果学生对所研究问题的看法受到学生个体特征和学校特征的影响,学生层面中学生成绩影响较大。结论多水平分析方法是处理分层嵌套数据的有用工具,利用两水平分层logistic回归模型可以同时探讨学生层面和学校层面解释变量对结局变量的效应,对于分析嵌套数据结构资料结局变量的影响因素有优越性。     

2型糖尿病合并高血压危险因素的病例对照研究

目的探讨影响2型糖尿病并发高血压的环境危险因素。方法采用成组病例对照研究设计,研究对象来自社区,其中病例108例,对照170例,采用非条件logistic回归模型分析糖尿病并发高血压的相关因素。结果单因素非条件logistic回归分析发现,糖尿病并发高血压与年龄显著相关(OR=2.573),根据年龄分层经单因素非条件logistic回归分析,发现有统计学意义(P<0.05)的变量有饮茶、食用腌制鱼或肉、食用两种以上烹调油、以米为主食。进一步进行多因素logistic回归分析,这些因素也均有统计学意义(P<0.05)。结论饮茶、食用腌制鱼或肉、食用两种以上烹调油为该人群患糖尿病合并高血压的危险因素,以米为主食可能为保护因素。     

Logistic回归、CMH检验与Meta分析在多中心临床试验中应用探讨

目的探讨临床随机对照试验多中心效应比较的统计方法。方法以一项多中心临床随机对照试验数据为例,运用χ2检验、CMH检验、Meta分析及logistic回归分析。结果CMH检验显示各中心间效应值的一致性检验差异有统计学意义(P<0.05),扣除中心效应后,组间比较差异有统计学意义(P<0.05);Meta分析异质性检验差异无统计学意义(P>0.05),采用固定效应模型,合并后效应值组间差异有统计学意义(P<0.05),logistic回归分析,各中心效应值差异无统计学意义(P>0.05),组间效应差异有统计学意义(P<0.05),且存在可能影响效应的协变量。结论多中心临床随机对照试验研究中,如果存在分中心组间疗效差异趋势不一致时,可选择Meta分析及logistic回归分析,然后对三种分析方法的结果作出客观的评价。如果logistic回归分析存在影响效应的协变量,建议对这些协变量进行再分析。     

2型糖尿病合并高血压危险因素的病例对照研究

目的探讨糖尿病合并高血压的危险因素。方法采用以社区为基础的成组病例对照研究设计,对2型糖尿病患者中300例合并高血压的病例和300例未合并高血压的对照进行问卷调查、体格检查和实验室检测。利用单因素和多因素非条件logistic回归模型分析2型糖尿病合并高血压的相关危险因素。结果根据年龄调整经单因素非条件logistic回归分析,差异有统计学意义(P0.05)的变量有糖尿病家族史、高血压家族史、日常体育锻炼情况、生活工作压力、体质指数、腰臀比、空腹血糖、餐后2h血糖、收入水平、水果摄入量。进一步进行多因素logistic回归分析,这些因素差异也均有统计学意义(P0.05)。结论糖尿病合并高血压是遗传和环境多因素综合作用所致,在防治过程中,应采取综合防治的策略,预防疾病的发生。     

临床试验中多个终点变量同时评价的多元logistic模型

目的研究临床试验中多个终点变量的同时分析。方法采用多元logistic回归模型,通过对原始资料的格式作适当变换,构造一个虚拟水平,视结果变量为1水平上的观察单位,以患者作为2水平单位,建立2水平logistic模型,对试验组和对照组的疗效,以及患者的年龄,性别,观察指标的基线值,中心效应等协变量进行分析。结果多终点的多元logistic回归模型既可以对单个结果变量进行分析,还可以对多个结果变量进行同时分析,并在扣除组间差异、协变量的影响后,估计两个结果变量之间的相关性。当受试者的多个结果有部分缺失时,该估计方法仍然是有效的。结论多元logistic回归模型可以对多个终点变量进行同时分析。     

核函数logistic回归模型在全基因组关联研究中的应用

[导读]探讨基于基因水平的核函数logistic回归模型及其在全基因组关联研究中的应用.以全基因组关联研究模拟数据为例,介绍核函数logistic回归模型在基因水平检测遗传变异与复杂性疾病之间关联的分析策略.模拟结果表明,在所有已知基因检验结果中致病位点所在基因假设检验的P值最小.结果提示基于基因水平的核函数logistic回归模型能够充分提取和综合基因中多个遗传突变位点信息,降低统计学检验的自由度,同时还能够控制多种协变量因素和交互作用,在检测致病基因与疾病关联时具有一定的效能.     

多水平模型在吸烟行为研究中的应用

目的介绍多水平模型及其应用领域。方法以中国/WHO控烟能力建设合作项目——学校控烟子项目中天津地区中学的学生基线调查资料为例,应用多水平模型分析并与传统logistic回归分析的结果进行比较。结果经检验,数据中存在层次结构。利用多水平模型分析显示,中学生吸烟的影响因素为性别、年龄、态度、环境及班级水平下的宣传教育。此外,在未引入班级水平下宣传教育这一变量时,利用多水平模型分析得到变量的标准误均小于相应的传统logistic回归分析的结果。结论多水平模型适于分析具有层次结构的数据资料,在分层或整群的流行病学或社区调查中具有较高的应用价值。     

冶金行业职工胆石病危险因素的病例对照研究

目的 探索胆石病的危险因素，为人群预防提供依据。方法 选取马钢公司胆系疾病现况调查中发现的175例胆石病患为病例组，421例非胆石病为对照组，进行人群病例对照研究；采用非条件logistic回归对38个变量进行分析。结果 单因素logistic回归分析发现共有20个变量有统计学意义，多因素logistic回归共有13个进入模型；其中7个变量为胆石病的危险因素，OR值大小依次为：胆囊炎史、胆石病家族史、油腻饮食、甘油三脂、体质指数、多食动物蛋白和女性孕次，而多饮牛奶、按时进早餐、饮酒、多食水果、较高化程度和多食蔬菜6个变量为保护因素。结论 胆囊炎、家族史、油腻饮食、高甘油三脂、肥胖、多食动物蛋白、多孕次是胆石病危险因素，而多吃蔬菜、水果、按时进早餐、多饮牛奶、加强自我保健意识有助于预防胆石病。     

职业性铅暴露人群血铅的危险因素研究

目的探讨职业铅暴露人群血铅的危险因素,为采取针对性的防控措施提供科学依据。方法选择余姚市蓄电池工厂227名工人作为研究对象,采用统一调查表对个人职业史、生活习惯、教育程度等进行调查,同时进行血铅和车间空气中铅浓度测定。在此基础上,分别采用单因素、多因素条件logistic回归分析筛选血铅的关联因素,P<0.05为差异有统计学意义。结果本研究共设29个检测点,仅5个点合格,合格率17.24%,工人平均血铅水平为(578.54±175.56)μg/L。单因素条件logistic回归分析显示性别、吸烟、户籍、文化程度和车间空气铅浓度等变量均可影响血铅浓度。多因素条件logistic回归分析显示车间空气铅浓度和户籍差异有统计学意义。结论工作场所铅浓度较高,工人血铅普遍较高,车间空气铅浓度和户籍是血铅增高的危险因素。     

Individual,familial and community determinants of child physical abuse among high-school students in China

While many risk factors for child physical abuse are known, little research exists examining these in multilevel contexts including both individual and environmental influences. The authors examined the roles of individual-, family- and community-level factors such as socioeconomic status (SES) in determining the likelihood of child physical abuse in Guangzhou, China. Twenty-four schools were recruited by stratified random sampling, with 6628 junior high-school students aged 13–16 years participating. Parental child physical abuse experience, together with family and community levels of SES among students were measured and their relationships were investigated by applying univariable, multivariable and multilevel logistic regression models. Univariable, multivariable and multilevel logistic regression models were applied. Six-month prevalence of minor, severe and very severe assaults were 23.2%, 15.1% and 2.8%, respectively. A U-shaped association between family SES and likelihood of severe assaults was identified. In the multilevel model, indicators of low family SES, mother's higher occupational and educational status remained significantly independent predictors of physical abuse. Internal migration status was associated with higher risk as was younger age. The authors suggest that previous categories of risk factors for physical abuse may be too simplistic, and that further research on social and environmental influences may usefully inform intervention programs.     

A multilevel analysis of the determinants and cross-national variations of HIV seropositivity in sub-Saharan Africa: evidence from the DHS

This paper applies multilevel logistic regression models to Demographic and Health Survey data collected during 2003–2008 from 20 countries of sub-Saharan Africa to examine the determinants and cross-national variations in the risk of HIV seropositivity in the region. The models include individual-level and contextual region/country-level risk factors. Simultaneous confidence intervals of country-level residuals are used to compare the risk of being HIV seropositive across countries. The study reveals interesting general patterns in the risk of HIV seropositivity in sub-Saharan Africa. In particular, the findings highlight the gender disparity in socio-economic risk factors, partly explained by sexual behaviour factors.     

Public health application comparing multilevel analysis with logistic regression: immunization coverage among long-term care facility residents

PURPOSE: Public health studies often sample populations using nested sampling plans. When the variance of the residual errors is correlated between individual observations as a result of these nested structures, traditional logistic regression is inappropriate. We used nested nursing home patient data to show that one-level logistic regression and hierarchical multilevel regression can yield different results. METHODS: We performed logistic and multilevel regression to determine nursing home resident characteristics associated with receiving pneumococcal immunizations. Nursing home characteristics such as type of ownership, immunization program type, and certification were collected from a sample of 249 nursing homes in 14 selected states. Nursing home resident data including demographics, receipt of immunizations, cognitive patterns, and physical functioning were collected on 100 randomly selected residents from each facility. RESULTS: Factors associated with receipt of pneumococcal vaccination using logistic regression were similar to those found using multilevel regression model with some exceptions. Predictors using logistic regression that were not significant using multilevel regression included race, speech problems, infections, renal failure, legal responsibility for oneself, and affiliation with a chain. Unstable health conditions were significant only in the multilevel model. CONCLUSIONS: When correlation of resident outcomes within nursing home facilities was not considered, statistically significant associations were likely due to residual correlation effects. To control the probability of type I error, epidemiologists evaluating public health data on nested populations should use methods that account for correlation among observations.     

胃癌病因学研究中交互作用研究方法的比较

本文对原发性胃癌病例对照研究中筛选出的危险因素：不良饮食嗜好、常食咸菜、吸烟量、食用陈旧食品等运用三种不同的交互作用研究方法，即叉生分析，多因素ｌｏｇｉｓｔｉｃ回归模型中加入交互作用项和使用ＧＬＩＭ软件进行广义相对危险度分析等作了分析和比较；并指出了各自的优缺点。正确识别因素间联合作用的模式对于疾病的一级预防和公共卫生决策的制订具有重要意义。     

多水平零膨胀计数模型在骨关节疼痛影响因素中的应用研究

目的针对两地区村民骨关节调查资料组群结构且零过多问题,阐明多水平零膨胀计数回归模型。方法介绍多水平Poisson和多水平ZIP模型原理,进一步完成山西省农村地区居民风湿性骨关节疼痛部位数影响因素分析的多水平模型SAS软件实现。结果多水平ZIP回归部分的随机项表明,两调查地区不同村庄居民关节是否疼痛以及疼痛部位数差别有统计学意义。logistic回归估计参数表明年龄、婚姻状况和高血压是影响关节疼痛与否的主要因素。而Pois-son部分估计结果表明年龄、地区、性别和地区交互作用是居民骨关节疼痛部位数的主要因素。Vuong检验进一步证实多水平ZIP模型比多水平Poisson模型更优。结论多水平ZIP回归模型是解决调查研究中组群结构及零发生次数过多问题模型拟合的最佳选择。     

Regression methods for estimating attributable risk in population-based case-control studies: a comparison of additive and multiplicative models

A regression method that utilizes an additive model is proposed for the estimation of attributable risk in case-control studies carried out in defined populations. In contrast to previous multivariate procedures for the estimation of attributable risk, which have utilized logistic regression techniques to adjust for confounding factors, the model assumes an additive relation between the covariates included in the regression equation. As an empirical example, additive and logistic models were fitted to matched case-control data from a population-based study of childhood astrocytoma brain tumors. Although both models fitted the data well, the additive model provided a more satisfactory estimate of the risk attributable to multiple exposures, in the absence of significant additive interaction. In contrast to the results from the logistic model, the adjusted estimates of the risk attributable to each factor included in the additive model summed to the overall estimate for all of the factors considered jointly. Thus, the additive approach provides a useful alternative to existing procedures for the multivariate estimation of attributable risk when the additive model is determined to be appropriate on the basis of goodness-of-fit.     

On the prediction and prevention of myocardial infarctions: models based on retrospective and doubly censored prospective data

Early detection of coronary heart disease (CHD) in its pre-clinical stages may offer a way to reduce the impact of this endemic disease on society. Coronary calcification accompanies the development of an atherosclerotic plaque, the pathological substrate of CHD, and its identification is currently possible by means of electron beam tomography (EBT). This is a non-invasive imaging test that quantifies the extent of coronary artery plaque calcification by means of a calcium score. In this study, we show that an age-sex based calcium score percentile (CS%) provides an invaluable predictor for myocardial infarction (MI), and examine how CS% is related to traditional risk factors. We study two separate groups of patients: 172 patients who underwent EBT screening after surviving an MI (retrospective group); and 676 asymptomatic subjects who were screened and followed for several years for the occurrence of an MI (prospective group). We use CS% with traditional risk factors in logistic regression models to: (1) compare patients in the retrospective and prospective groups, and (2) develop a mortality model for MIs that occurred in the prospective group. These logistic regressions are used to develop a joint model for the relative log-odds of an MI, which is non-linear in the covariates of the mortality model. We also use baseline covariates in the prospective group to fit an event-time regression model and estimate probabilities for 2 and 3 year exposure periods. The event-time regression provides independent estimates of the relative odds that are associated with risk factors. CS%, smoking, and their interaction were preeminent as predictors in the joint model for the relative odds of an MI and in the event-time regression, although the effects of smoking and CS% were generally subadditive. These models provide important information on the risks associated with elevated CS% levels.     

多层模型方法及其在血吸虫病传播风险因子识别研究中的应用

目的将多层模型方法引入到血吸虫传播风险因子识别的研究,探讨影响血吸虫病传播的因素。方法针对西昌地区17个村组2000～2004年血吸虫病调查数据的分层特点,利用多层方法对数据分析,并将空间因素纳入模型中进行分析。结果血吸虫病发病率与年龄、性别、钉螺感染密度、作物种植结构等因素具有显著的相关关系。结论多层数据方法能够应用于流行病调查数据分析,并可以取得较好的结果。     

Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression

The logistic regression model is frequently used in epidemiologic studies, yielding odds ratio or relative risk interpretations. Inspired by the theory of linear normal models, the logistic regression model has been extended to allow for correlated responses by introducing random effects. However, the model does not inherit the interpretational features of the normal model. In this paper, the authors argue that the existing measures are unsatisfactory (and some of them are even improper) when quantifying results from multilevel logistic regression analyses. The authors suggest a measure of heterogeneity, the median odds ratio, that quantifies cluster heterogeneity and facilitates a direct comparison between covariate effects and the magnitude of heterogeneity in terms of well-known odds ratios. Quantifying cluster-level covariates in a meaningful way is a challenge in multilevel logistic regression. For this purpose, the authors propose an odds ratio measure, the interval odds ratio, that takes these difficulties into account. The authors demonstrate the two measures by investigating heterogeneity between neighborhoods and effects of neighborhood-level covariates in two examples--public physician visits and ischemic heart disease hospitalizations--using 1999 data on 11,312 men aged 45-85 years in Malmo, Sweden.     

主成分Logistic回归在筛选冠心病危险因素中的应用

目的探讨主成分Logistic回归方法在筛选冠心病危险因素中的应用。方法选择2009年4月-2010年8月在哈尔滨医科大学第一附属医院行冠脉造影术者,按照金标准分为冠心病组（465例）和对照组（277例）;首先对Lo-gistic回归模型进行共线性诊断,然后应用主成分改进的Logistic回归分析,得到并解释最终的回归模型。结果共线性诊断提示各变量之间存在明显的共线性,采用主成分改进的Logistic回归分析显示,冠心病与年龄、性别、载脂蛋白A、TG、HDL-C、糖尿病史、高血压史、吸烟史在内的多种因素有关。结论主成分改进的Logistic回归在筛选冠心病危险因素中具有较好的作用,在对疾病危险因素进行Logistic回归分析时,若多变量间存在多重共线性,采用主成分改进的Lo-gistic回归分析能得出更好的回归模型。