对数线性模型在病例-父母对照研究中的应用

介绍应用对数线性模型分析病例-父母对照研究设计的方法.以亚甲基四氢叶酸还原酶基因(MTHFR)C677T与唇腭裂的关联研究为例,应用对数线性模型分析母亲、子代基因及其交互作用与唇腭裂的关系.结果显示变异型纯合子母亲的子代发生唇腭裂的风险低于野生型纯合子母亲的子代,S2=0.43 (95％CI:0.19 ～ 0.95),未发现唇腭裂与子代基因及母子交互作用相关.应用对数线性模型分析病例-父母对照研究设计的方法尤其适用于妊娠期疾病与源于胚胎时期疾病等的病因学研究.     

对数线性模型在骨折资料分析中的应用

目的应用对数线性模型对骨折资料进行分析.方法收集了天津医院1993～2002年的骨折病历资料,采用对数线性模型对上述资料进行分析.结果骨折患者的年龄,性别和骨折原因间存在着交互作用.结论对数线性模型可以更好的分析多个变量间的交互作用,适用于探索性的研究.     

对数线性模型分析在煤工尘肺生命质量评价中的应用

对数线性模型分析是一种处理复杂列联表资料的重要方法。本文着重介绍了煤工尘肺生命质量评价中的对数线性模型分析结果及专业解释。此方法在探讨因素间交互作用方面比相关回归分析更前进了一步。提供了定量比较的数据，有助于生命质量评价的统计分析工作。     

应用对数线性模型分析影响母乳喂养的原因

本文分析了山东省6264名4～12个月婴儿的喂养方式及改变母乳喂养的原因,应用对数线性模型的方法,对改变母阿爸年的原因以及影响母乳分泌的因素进行了分析。分析表明:直接影响母乳喂养的主要因素是母乳分泌不足;母亲年龄增大,产后24小时内非母婴同室,产后开奶时间晚于24小时等。影响婴儿母乳喂养的原因是多方面的,各因素之间存在着复杂的交互影响。本次分析显示:母亲年龄≥24岁、职业为非农民者,无论其有否母乳分泌不足的因素,非母乳喂养的危险性均明显增高;有非母乳喂养家族史者,虽然开奶时间早,没有母乳分泌不足的因素,其非母乳喂养的危险性也明显增加。     

混合线性模型的应用

目的介绍混合线性模型的结构，固定效应项和随机效应的含义。对具有内部相关性的资料．宣选用混合线性模型进行配合。方法用一个具有聚集性结构的例子和一个重复测量的例子说明配合混合线性模型的方法和步骤。结果分析了资料的层次结构，识别不同层次上的协变量，讨论了模型中固定效应矩阵和随机效应矩阵的结构，使模型参数估计值更易于理解和解释。由于混合线性模型克服了一般线性模型对反应变量必须具有独立和等方差的要求，从而扩大了线性模型的应用范围。对于具有聚集性质的资料及重复测量资料具有很好的拟合效果。结论这一模型计算较复杂，应用SAS／STAT软件包中的PROMIXED过程能很好地解决计算问题。     

APC泊松对数线性模型及其在肿瘤流行病研究中的应用

[目的]结合实例探讨APC泊松对数线性模型的结构及应用条件;阐明年龄、观察时期和出生队列对乌鲁木齐地区恶性肿瘤死亡率的影响,为恶性肿瘤的预防提供科学的依据.[方法]以乌鲁木齐地区恶性肿瘤死亡病例为研究对象,系统收集该地区1978～1997年恶性肿瘤死亡登记资料,采用SAS软件分析资料.[结果]30岁以上各年龄组患恶性肿瘤的危险性快速增大,30岁以上各组分别与15～19岁年龄组相比差别明显(P<0.05),与1978～1982年相比,各观察时期的作用不明显(P>0.05),在所有出生队列中,1914～1942年出生的人群患恶性肿瘤的危险性最大.[结论]乌鲁木齐地区恶性肿瘤死亡率受年龄和出生队列的影响;泊松对数线性模型在应用中为了获得可靠的专业结论需采用合理的数据压缩方法.     

对数线性模型在评分者一致性研究方法中的应用北大核心CSCD

目的研究对数线性模型在等级资料评分者一致性研究方法中的应用。方法由高级和中级及以下职称的医生独立收集慢性支气管炎的四诊信息,以口唇青紫程度为例,其评分为4个等级。采用不同的对数线性模型对观测频数进行拟合,选择拟合程度好的模型,再进行一致性结构分析。结果对三个对数线性模型进行比较,得到等权重模型的结果最好,表明高级和中级及以下职称的医生之间存在正相关。结论在一致性研究中应用对数线性模型,可以分析一致性结构,对特定的假设进行检验,不再是应用单个统计量总结一致性。     

纵向观测计数数据的对数线性模型

纵向观测数据是按时间顺序对个体的某一变量进行多次人观测获得的资料。本文利用广义线性模型对同计数数据进行了分析,充分考虑重复观测间的相关性。方法采用Ｚｅｇｅｒ和Ｌｉａｎｇ提出的广义估计方程在拟对数广义线性模型的同时,引入偏离参数,讨论三种协方差矩阵的结构。结果同时获得的回归参数,相关参数,偏离参数的估计,完成了较为实用的运行程序,并进行了实例讨论。结论医学研究和临床试验中经接触到纵向观测数据,对这类     

室内公共场所空气质量指标的对数线性模型分析

「目的」对公共场所多种空气质量分类指标间的关系,用多维列联表来表达;分析气温（ＴＥＭ）、负离子浓度（ＮＩ）、二氧化碳（ＣＯ２）含量对空气中的细菌总数（ＢＡＣ）的影响;用对数线性模型来评价效应的主次和估计交互作用的大小。「方法」收集５家歌舞厅６６个监测点的上述４个空气质量指标的数据,以ＳＰＳＳ１０．０ｆｏｒＷｉｎｄｏｗｓ建立数据库并进行数据处理。用对数线性模型分析。「结果」通过其最佳模型,发现影响空气中细菌总数多少的主要因素是二氧化碳与负离子浓度,其中ＣＯ２＊ＢＡＣ交互作用最大,参数估计为＋０．４６７３;其次是ＣＯ２＊ＮＩ,参数估计为－４２４６。「结论」二氧化碳含量与负离子浓度均会影响空气中的细菌总数,前者为正向影响,后者为反向影响。增加通风以减少二氧化碳蓄积并增设负离子发生器,可减少室内公共场所空气中的细菌总数     

混合线性模型在军事训练重复测量资料中的应用

目的探讨混合线性模型在有关军事训练重复测量资料分析中的价值。方法采用SAS软件中的MIXED过程对部队士兵进行100m跑前后各时间点采集的心率、血氧饱和度进行分析。结果混合线性模型对数据的完整性、反应变量的独立性和方差齐性不作要求，从而扩大了线性模型的适用范围，而且能得到个体化的分析结果，大大提高了现有资料的利用效率。结论混合线性模型能对军事训练中获取的重复测量资料准确、全面地进行分析，为部队改进训练方法、增强战斗力服务，因此，具有很高的应用价值。     

A hybrid design: case-parent triads supplemented by control-mother dyads

Hybrid designs arose from an effort to combine the benefits of family-based and population-based study designs. A recently proposed hybrid approach augments case-parent triads with population-based control-parent triads, genotyping everyone except the control offspring. Including parents of controls substantially improves statistical efficiency for testing and estimating both offspring and maternal genetic relative risk parameters relative to using case-parent triads alone. Moreover, it allows testing of required assumptions. Nevertheless, control fathers can be hard to recruit, whereas control offspring and their mothers may be readily available. Consequently, we propose an alternative hybrid design where offspring-mother pairs, instead of parents, serve as population-based controls. We compare the power of our proposed method with several competitors and show that it performs well in various scenarios, though it is slightly less powerful than the hybrid design that uses control parents. We describe approaches for checking whether population stratification will bias inferences that use controls and whether the mating-symmetry assumption holds. Surprisingly, if mating symmetry is violated, even though mating-type parameters cannot be directly estimated using control-mother dyads alone, and maternal effects cannot be estimated using case-parent triads alone, combining both sources of data allows estimation of all the parameters. This hybrid design can also be used to study environmental influences on disease risk and gene-by-environment interactions.     

Pseudosibship methods in the case-parents design

Recent evidence suggests that complex traits are likely determined by multiple loci, each of which contributes a weak to moderate individual effect. Although extensive literature exists on multilocus analysis of unrelated subjects, there are relatively fewer strategies for jointly analyzing multiple loci using family data. Here we address this issue by evaluating two pseudosibship methods: the 1:1 matching, which matches each affected offspring to the pseudosibling formed by the alleles not transmitted to the affected offspring, and the exhaustive matching, which matches each affected offspring to the pseudosiblings formed by all the other possible combinations of parental alleles. We prove that the two matching strategies use exactly and approximately the same amount of information from data under additive and multiplicative genetic models, respectively. Using numerical calculations under a variety of models and testing assumptions, we show that compared with the exhaustive matching, the 1:1 matching has comparable asymptotic power in detecting multiplicative/additive effects in single-locus analysis and main effects in multilocus analysis, and it allows association testing of multiple linked loci. These results pave the way for many existing multilocus analysis methods developed for the case-control (or matched case-control) design to be applied to case-parents data with minor modifications. As an example, with the 1:1 matching, we applied an L1 regularized regression to a Crohn's disease dataset. Using the multiple loci selected in our approach, we obtained an order-of-magnitude decrease in p-value and an 18.9% increase in prediction accuracy when compared with using the most significant individual locus.     

Log-linear model selections in a rural dental health study

This field study sought to measure the effects of dental delivery and school-based, dental health education on use of dental health care by children in grades K-6. We attempted to control for two potential confounding factors by an approximate randomization of children into treatment groups with stratification on grade and initial oral disease levels. A backward elimination log-linear model selection procedure for the 5-factor classification permitted tests for higher-order interaction, namely effect-modification, confounding and collapsibility. We found that the effect of dental health education on use of dental care depended on the mode of dental delivery.     

Testing for association with a case-parents design in the presence of genotyping errors

Genotyping errors can create a problem for the analysis of case-parents data because some families will exhibit genotypes that are inconsistent with Mendelian inheritance. The problem with correcting Mendelian inconsistent genotype errors by regenotyping or removing families in which they occur is that the remaining unidentified genotype errors can produce excess type I (false positive) error for some family-based tests for association. We address this problem by developing a likelihood ratio test (LRT) for association in a case-parents design that incorporates nuisance parameters for a general genotype error model. We extend the likelihood approach for a single SNP to include short haplotypes consisting of 2 or 3 SNPs. The extension to haplotypes is based on assumptions of random mating, multiplicative penetrances, and at most a single genotype error per family. For a single SNP, we found, using Monte Carlo simulation, that type I error rate can be controlled for a number of genotype error models at different error rates. Simulation results suggest the same is true for 2 and 3 SNPs. In all cases, power declined with increasing genotyping error rates. In the absence of genotyping errors, power was similar whether nuisance parameters for genotype error were included in the LRT or not. The LRT developed here does not require prior specification of a particular model for genotype errors and it can be readily computed using the EM algorithm. Consequently, this test may be generally useful as a test of association with case-parents data in which Mendelian inconsistent families are observed.     

新型杂交手术室的设计

目的:研究满足现代介入手术需求、布局合理、实用性强、有一定前瞻性的杂交手术室设计方案.方法:通过设计规划新建放射外科大楼,综合考虑新建杂交手术室的机房面积、功能分区、操作流程、无菌规范等各种因素,设计符合介入手术和开放性手术操作要求,功能全面的杂交手术室.结果:充分论证、科学设计是建设杂交手术室的重要环节,将为临床医师提供更多手术方式的选择,是医疗技术发展的需求.结论:规划新建数字减影血管造影(DSA)手术室时,要综合考虑各种因素,严格按照无菌操作流程规划,合理分布各个功能区块；结合开放性手术和介入手术功能,设计具有科学性、实用性和一定的前瞻性的杂交手术室.     

2 173例骨折住院患儿资料的分析——对数线性模型的应用

目的 了解湖南省骨折住院患儿影响因素间的交互作用,为儿童骨折的防治制定有针对性的措施。方法 采用对数线性模型对儿童骨折影响因素间的交互项进行筛选与分析。结果 不同类型骨折住院患儿的治愈率（（口恶）²=51.036,P<0.001）、住院日（H=67.101,P<0.001）和住院费用（H=46.282,P<0.001）均有统计学差异;骨折住院患儿的骨折原因与骨折类型两者之间存在交互作用,跌落易导致尺桡骨骨折,而交通事故则易出现胫腓骨骨折;骨折住院患儿的性别、年龄与骨折原因三者之间存在交互作用,0~3岁男孩易由交通事故和跌落这两种伤害类型导致骨折;骨折住院患儿的性别、年龄与骨折类型三者之间也存在交互作用,0~3岁儿童,不论男孩女孩,发生尺桡骨骨折和肱骨骨折这两种骨折类型可能性比较低。结论 应根据不同年龄,性别,骨折原因以及骨折类型采取有针对性的措施预防儿童骨折;对数线性模型可以更好的用于分析多个变量间的交互作用,适用于探索性的研究。     

An objective Bayesian analysis of a crossover design via model selection and model averaging

Inference about the treatment effect in a crossover design has received much attention over time owing to the uncertainty in the existence of the carryover effect and its impact on the estimation of the treatment effect. Adding to this uncertainty is that the existence of the carryover effect and its size may depend on the presence of the treatment effect and its size. We consider estimation and testing hypothesis about the treatment effect in a two‐period crossover design, assuming normally distributed response variable, and use an objective Bayesian approach to test the hypothesis about the treatment effect and to estimate its size when it exists while accounting for the uncertainty about the presence of the carryover effect as well as the treatment and period effects. We evaluate and compare the performance of the proposed approach with a standard frequentist approach using simulated data, and real data. Copyright © 2016 John Wiley & Sons, Ltd.     

不完全病例对照研究基因环境交互作用的估计

目的 介绍不完全病例对照研究中基因与环境交互作用的估计方法.方法 分别导出了logistic模型、对数线性模型在传统病例对照研究、单纯病例研究、不完全病例对照研究中主效应以及基因与环境交互作用效应的极大似然估计,并通过实例分析其应用价值.结果 在传统病例对照研究中,当数据未缺失时,logistic模型与对数线性模型的结果是等价的.当无对照时,单纯病例研究的logistic模型可以估计基因与环境的交互作用.当对照组基因信息缺失但环境信息齐全时,用传统病例对照研究的logistic模型无法得到交互作用的估计;用单纯病例研究的logistic模型可以估计交互作用,但由于没有充分利用环境的信息,故得不到环境主效应的估计;不完全病例对照研究的对数线性模型,可同时得到交互作用和环境主效应的估计.结论 不完全病例对照研究采用对数线性模型既可充分利用对照的环境暴露信息,估计环境的主效应,又可估计基因与环境的交互作用.当基因与环境暴露独立时,其估计值与完全数据是等价的.     

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。