基于广义估计方程的多重应答资料统计分析方法

目的目前对于多重应答的统计推断尚无较好的方法,本文尝试对其进行分析时可用的模型结构作一探索.方法在选项可互换时,考虑使用选择总数进行分析;在选项不可互换时,将其视为多元logistic回归问题,采用广义估计方程加以分析,随后利用实例对上述思路作了进一步阐述.结果广义估计方程可以很好地分析自变量对于多重应答各项的影响,并能进一步对各选项间的相关和具体差异进行分析.结论广义估计方程可以作为多重应答统计分析问题的标准模型加以推广.     

广义估计方程在艾滋病知识多重应答资料中的应用

目的：探讨广义估计方程在多重应答资料中的运用。方法运用SPSS统计软件，将调查获得的艾滋病传播途径多重应答资料视为多因变量的多元Logistic回归，采用广义估计方程建模分析。结果发放调查问卷700份，回收607份，有效回收率86.71%。艾滋病传播途径总知晓率93.08%，其中经血传播知晓率最高（输血和共用注射器的知晓率分别为95.55%和96.71%）；其次为母婴传播和经性传播的知晓率（94.56%和85.50%）。广义估计方程分析显示性别、文化程度和年龄是影响艾滋病传播途径认知的因素。结论广义估计方程能较好地对多重应答资料进行统计分析与推断。     

用广义估计方程分析有序多分类重复测量资料

目的 探讨利用重复测量资料拟合广义估计方程,为重复测量资料分析提供方法学参考.方法采用SAS软件包中的GENMOD过程,结合中医针刺手法组与非手法组治疗周围性面瘫的多中心临床试验实例,比较有协变量和无协变量的广义估计方程分析结果.结果周围性面瘫的疗效指标为House Brackmann（HB）分级.以4次随访的HB分级为反应变量,以中心、组别、首次HB分级为自变量,建立有协变量的广义估计方程,结果显示：中心之间疗效差异无统计学意义,不存在中心效应;手法组和非手法组之间疗效差异有统计学意义,手法组疗效优于非手法组;不同首次HB分级之间疗效差异有统计学意义,基础病情对疗效有影响.以首次HB分级和4次随访的HB分级为反应变量,以中心、组别为自变量,建立无协变量的广义估计方程,结果显示：中心之间疗效差异有统计学意义,存在中心效应;手法组和非手法组之间疗效差异无统计学意义,尚不能认为两组疗效不同.结论广义估计方程可以有效控制层次相关性,在分析有序多分类重复测量资料时具有优势.     

广义估计方程在临床试验重复测量资料中的应用

目的：探讨广义估计方程在临床试验重复测量资料分析中的应用。方法：利用广义估计方程分析结果指标为分类变量的重复测量资料，通过参数和标准误的估计得出统计学结论。结果：对于临床试验重复测量资料，广义估计方程能有效的考虑组内相关性，处理有缺失值的资料，可以获得中心效应的参数及其标准误的估计值，以及在考虑了中心效应之后，可以有效估计处理因素有无作用及其作用大小。结论：采用广义估计方程对临床试验重复测量资料进行统计分析，可以使药物疗效评价更为客观。     

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。     

广义估计方程在交叉设计等级资料分析中的应用

目的：探讨广义估计方程在交叉试验等级资料中的应用，为临床试验交叉设计资料的正确分析提供方法学参考。方法：采用SAS软件包的GENMOD过程拟合模型，并以某治疗精神分裂症药物临床试验资料进行实例分析。结果：获得了各参数及其标准误的估计值，并可以对各因素进行直观的解释。结论：广义估计方程可以根据需要纳入如中心、年龄、性别、基线值等协变量进行分析，同时对缺失数据处理具有较好的稳健性，是处理交叉设计中等级资料的有效分析方法。     

生长监测纵向资料的纵横分析

以生长监测资料为例，用“广义估计方程”ＧＥＥ１从纵向角度，用逐步回归、典型相关从横向角度做综合分析。ＧＥＥ１体现的是整个婴儿期内自变量作用的总趋势，逐步回归则说明自变量在其中某个年龄段面作用的局部特征，典型相关还可比较变量在该段面的相对作用，故纵向资料的纵横分析有助于多方面挖掘同一资料的有益信息。文章还讨论了ＧＥＥ１在生长监测资料分析中的一些应用问题。     

累积残差在广义估计方程模型诊断中的应用

目的 探讨累积残差在广义估计方程模型诊断中的应用及SAS 9.1.3软件实现.方法 收集癫痫新疗法临床疗效资料,构建广义估计方程模型,根据QIC准则进行作业相关矩阵选择,采用累积残差检验固定效应及总体均数函数形式是否正确.结果 累积残差图直观明确,计算Kolmogorov-type supremum test概率值能客观地检验拟合模型效果,是值得推荐的一种方法.结论 对相关数据分析,构建广义估计方程时,采用累积残差技术进行模型诊断,可以获得更为客观的分析结论.     

非独立多分类数据的统计分析方法及SAS程序

目的采用广义估计方程、广义线性混合模型、非线性混合模型分析非独立分类数据。方法以眼科临床试验资料为实例,采用SAS9.2软件中GENMOD、GLIMMIX、NLMIXED过程拟合广义估计方程、线性混合效应模型、非线性混合效应模型。结果广义估计方程将非独立观测间的协方差结构处理为多余参数,模型中不能容纳随机效应;广义线性混合模型可以拟合多个随机效应,但不能对其提供统计学检验;非线性混合效应模型中因变量的分布形式可以自己指定,可以提供真实的似然函数及随机效应的统计学检验结果。3种分析方法的统计分析结论一致。结论 3种分析方法都是分析非独立分类数据的有力工具,研究者根据数据的特征及研究目的选择合适的方法对资料进行分析。     

广义估计方程在纵向资料中的应用

目的：探讨如何利用纵向资料拟合广义估计方程，为纵向资料分析提供方法学参考。方法：应用实例阐述纵向资料分析的特点和传统分析方法的不足，采用广义估计方程，解决实际存在的问题。结果：用药时间与临床疗效旱正相关（P〈0．0001）。试验组与对照组临床疗效差异有统计学意义（P=0．0413），试验组优于对照组。结论：在纵向资料分析中广义估计方程有一定的优势。     

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。     

工业噪声环境影响预测方法研究

目的选用工业噪声预测模型，预测建设项目营运后工业噪声源对环境受声点的影响，为工业噪声环境卫生学评价提供科学依据。方法以工业噪声源现状监测、影响预测及对建设项目营运后环境受声点的实测数据为依据，采用工业噪声预测模式，对工业噪声环境影响预测结果进行分析。结果采用工业噪声预测模式，可以较为准确地分析、预测建设项目营运后环境受声点噪声影响程度。结论介绍的工业噪声影响预测评价方法在开展区域环境卫生学评价中有一定的使用价值，不但能够起到噪声污染预评价的目的，而且可以为工业噪声污染源治理提供设计依据。     

Analysis of binary outcomes from randomised trials including multiple births: when should clustering be taken into account?

Randomised trials involving infants from both single and multiple births present unique statistical challenges. A range of methods have been used to analyse such data, including standard methods which treat all infants as independent, and more complex methods which account for the dependence between outcomes of infants from the same pregnancy. Conflicting recommendations have been made regarding if and when this dependence, or clustering, should be taken into account in the analysis. We studied the performance of ordinary logistic regression, which ignores the clustering, compared with logistic generalised estimating equations (GEEs) and mixed effects models (MEMs), which account for the clustering, using real and simulated datasets. Ordinary logistic regression produced appropriate type I error and coverage rates, provided the dependence between outcomes of infants from the same pregnancy was small and the multiple birth rate was low, but performed poorly otherwise. The type I error rate increased and the coverage rate decreased as either the strength of the dependence or the multiple birth rate increased. In contrast, logistic GEEs maintained appropriate type I error and coverage rates across a wide range of settings. The performance of logistic MEMs varied depending on the setting and the estimation procedure used but was often similar to or better than ordinary logistic regression. We recommend using a method which takes the clustering into account when analysing datasets including infants from multiple births.     

Recycling, channeling and heterogeneous protein turnover estimation using a model of whole-body protein turnover based on leucine kinetics in rodents

In the companion paper, a whole-body, mechanistic model of protein turnover in a rodent was described and evaluated with independent data sets that used the flooding dose method. On the basis of fitted fluxes, the model was able to predict specific radioactivity changes in the protein and free leucine pools and whole-body protein fractional synthesis rate (FSR). In this paper, results of model simulations of specific radioactivity changes in the flooding dose, pulse dose and continuous infusion methods were compared and the influence of recycling, channeling and multiple protein pools on model behavior were analyzed. For all methods, the percentage of channeling must be estimated to determine whether the extracellular or intracellular pool specific radioactivities better approximate the aminoacyl tRNA pool specific radioactivity. Recycling also affects the specific radioactivity of the aminoacyl-tRNA pool and therefore must be estimated. An analysis of fits of the flooding dose data indicated that 100% channeling was occurring, but the percentage of recycling could not be determined. Multiple protein pools turning over at different rates overestimated FSR by 2-3% at early time points (5 min) and underestimated FSR by 3-6% at 60 min in the flooding dose method. For the pulse dose method, FSR was underestimated by 40-50% at 5 min and underestimated by 9-10% at 60 min. An increase in time to measure FSR caused a decrease in the estimate of FSR (18% over 3 h) for the flooding dose method and an increase in the estimate of FSR (144% over 3 h) for the pulse dose method.     

多反应变量重复测量资料的协方差类型模型应用及SAS实现

目的 探讨协方差类型模型在多反应变量的重复测量资料分析中的应用方法 为了评价盐酸吡格列酮片治疗2型糖尿病的有效性,以安慰剂为对照,对240例2型糖尿病患者的空腹血糖和餐后2 h血糖重复观测数据进行多反应变量的协方差类型模型分析,对模型的固定效应参数矩阵作最小二乘估计并进行组间比较,同时给出误差效应的方差协方差矩阵,利用...     

Semiparametric Bayesian joint modeling of a binary and continuous outcome with applications in toxicological risk assessment

Many dose–response studies collect data on correlated outcomes. For example, in developmental toxicity studies, uterine weight and presence of malformed pups are measured on the same dam. Joint modeling can result in more efficient inferences than independent models for each outcome. Most methods for joint modeling assume standard parametric response distributions. However, in toxicity studies, it is possible that response distributions vary in location and shape with dose, which may not be easily captured by standard models. To address this issue, we propose a semiparametric Bayesian joint model for a binary and continuous response. In our model, a kernel stick‐breaking process prior is assigned to the distribution of a random effect shared across outcomes, which allows flexible changes in distribution shape with dose shared across outcomes. The model also includes outcome‐specific fixed effects to allow different location effects. In simulation studies, we found that the proposed model provides accurate estimates of toxicological risk when the data do not satisfy assumptions of standard parametric models. We apply our method to data from a developmental toxicity study of ethylene glycol diethyl ether. Copyright © 2013 John Wiley & Sons, Ltd.     

An autoregressive linear mixed effects model for the analysis of unequally spaced longitudinal data with dose-modification

The assessment of the dose-response relationship is important but not straightforward when the therapeutic agent is administered repeatedly with dose-modification in each patient and a continuous response is measured repeatedly. We recently proposed an autoregressive linear mixed effects model for such data in which the current response is regressed on the previous response, fixed effects, and random effects. The model represents profiles approaching each patient's asymptote, takes into account the past dose history, and provides a dose-response relationship of the asymptote as a summary measure. In an autoregressive model, intermittent missing data mean the missing values in previous responses as covariates. We previously provided the marginal (unconditional on the previous response) form of the proposed model to deal with intermittent missing data. Irregular timings of dose-modification or measurement can also be treated as equally spaced data with intermittent missing values by selecting an adequately small unit of time. The likelihood is, however, expressed by matrices whose sizes depend on the number of observations for a patient, and the computational burden is large. In this study, we propose a state space form of the autoregressive linear mixed effects model to calculate the marginal likelihood without using large matrices. The regression coefficients of the fixed effects can be concentrated out of the likelihood in this model by the same way of a linear mixed effects model. As an illustration of the approach, we analyzed immunologic data from a clinical trial for multiple sclerosis patients and estimated the dose-response curves for each patient and the population mean.     

按项目支付方式对恶性肿瘤参保患者住院费用的影响分析

目的 通过分析按项目支付的3个城市恶性肿瘤患者住院费用,探索不同医疗保险管理方式对恶性肿瘤治疗费用的影响.方法 从北京、上海、郑州市医保数据库中各随机抽取恶性肿瘤医保患者600名,统计分析其住院费用及其影响因素,并用多元线性回归模型分析次均住院费用的影响因素.结果 不同的管理方式对住院费用产生一定的影响,从而对住院费用有不同程度的影响.结论 在制定医保管理方法时,不仅要考虑控费效果,还应保障患者合理的就医需要;多种结算方式的综合应用可弥补单一结算方式的不足.     

双反应变量重复测量资料分析及MIXED过程实现

目的探讨双反应变量重复测量资料的分析原理与方法及SAS软件PROCMIXED过程的应用。方法结合双反应变量重复测量数据的特点,采用SAS软件的MIXED过程对其进行分析,建立线性混合效应模型。结果该模型不仅考虑了每个变量多次重复测量结果之间的相关性,也考虑了两个变量之间的相关性,同时还引入固定效应和随机效应,结合数据特征分析,结果更为可信。结论对双反应变量非独立重复测量资料,可以把数据之间的相关性分解为重复测量间相关性和变量间相关性两部分,采用MIXED过程不仅可对其相关性做出明晰深入的分析,且可保证数据分析结果解释更符合实际。