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。     

分层整群随机抽样数据的不同分析方法及结果比较

目的探讨分层整群抽样数据应用SAS9.1分析时,不同分析方法对结果的影响。方法比较多因素logistic回归,surveylogistic回归以及广义线性混合效应模型(glimmix)在分层整群抽样数据中的统计分析,并用实例加以说明。结果不同的方法所产生的结果是有差别的。surveylogistic回归与广义线性混合效应模型对模型中各回归系数的标准误进行了调整,使得其比多因素logisitic回归中的标准误大。在实例分析中各危险因素的OR值也发生了变化,其95%可信区间都有不同程度的增宽。结论在分层整群抽样中,为减少模型系数标准误估计的向下偏倚以及第Ⅰ类错误的发生,surveylogistic回归与广义线性混合效应模型都是比较适用的,不建议使用多因素logisitic回归。     

重复测量诊断试验的ROC曲线广义线性混合效应模型北大核心CSCD

目的针对重复测量诊断数据,为同时考虑协变量对诊断试验准确性评价的影响,度量重复测量数据间的相关性,本文探索新的ROC曲线的建模方法。方法通过广义线性混合效应模型对ROC曲线进行模拟,并采用贝叶斯参数估计方法,利用Win BUGS软件予以实现,进而计算不同协变量取值下的ROC曲线下面积(AUC)以对诊断试验结果进行评价。结果实例数据分析结果表明,基于广义线性混合效应模型的ROC曲线建模方法可以有效地刻画重复测量诊断试验数据,给出更有解释意义的回归参数,提供临床分析的参考依据。结论基于广义线性混合效应的ROC曲线模型在解决重复测量诊断试验的准确度评价问题起着至关重要的作用。     

重复测量诊断试验的ROC曲线广义线性混合效应模型

目的针对重复测量诊断数据,为同时考虑协变量对诊断试验准确性评价的影响,度量重复测量数据间的相关性,本文探索新的ROC曲线的建模方法。方法通过广义线性混合效应模型对ROC曲线进行模拟,并采用贝叶斯参数估计方法,利用Win BUGS软件予以实现,进而计算不同协变量取值下的ROC曲线下面积(AUC)以对诊断试验结果进行评价。结果实例数据分析结果表明,基于广义线性混合效应模型的ROC曲线建模方法可以有效地刻画重复测量诊断试验数据,给出更有解释意义的回归参数,提供临床分析的参考依据。结论基于广义线性混合效应的ROC曲线模型在解决重复测量诊断试验的准确度评价问题起着至关重要的作用。     

重复测量线性混合模型中方差-协方差结构的选择

线性混合效应模型在医学重复测量资料的数据分析与设计中广泛应用。本文围绕医学实例阐述了重复测量线性混合效应模型在对不同方差 -协方差结构建模时协方差结构的选择 ,并对结果进行分析 ,为重复测量线性混合效应模型在医学研究中的应用提供了方便     

混合效应模型在系统分组资料中的应用及SAS实现

目的 探讨系统分组资料的混合效应模型的建模及SAS程序实现.方法 以系统分组资料例证混合效应模型的SAS MIXED 实现过程,并与传统的广义线性模型结果作比较.结果 该实例显示SAS混合效应模型结果与广义线性结果一致.结论 SAS MIXED可以灵活地拟合和分析系统分组资料.     

分类重复测量数据的非线性混合效应模型

[目的]探讨分类重复测量数据的非线性混合效应模型及SAS8.0软件NLMIXED过程实现。[方法]直接拟合分类反应变量的非线性概率模型,结合重复测量资料的特点,采用附加高斯积分来获得最大似然的参数估计。[结果]非线性混合效应模型能很好地拟合分类反应变量的重复测量资料,它允许固定效应和随机效应进入模型的非线性部分,可方便地分析随机缺失等非均衡数据。[结论]分类反应变量重复测量资料的非线性混合效应模型分析结果合理、容易解释,为分类重复测量资料提供一种新的分析思路。     

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。     

医疗器械临床试验定量指标纵向数据中不同模型的比较研究

目的 通过比较一般线性模型、广义估计方程及混合效应模型在医疗器械试验定量指标纵向数据中的估计效果,探讨合适分析方法并提供参考.方法 以非劣效支架试验为例并结合应用蒙特卡洛随机模拟的方法,模拟不同样本量、不同数据相关程度及不同相关数据比例下的纵向数据,比较不同模型应用的准确性.结果 在不同数据相关程度及相关数据比例下,不...     

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     

A note on genetic variance components in mixed models

Burton et al. ([1999] Genet. Epidemiol. 17:118-140) proposed a series of generalized linear mixed models for pedigree data that account for residual correlation between related individuals. These models may be fitted using Markov chain Monte Carlo methods, but the posterior mean for small variance components can exhibit marked positive bias. Burton et al. ([1999] Genet. Epidemiol. 17:118-140) suggested that this problem could be overcome by allowing the variance components to take negative values. We examine this idea in depth, and show that it can be interpreted as a computational device for locating the posterior mode without necessarily implying that the original random effects structure is incorrect. We illustrate the application of this technique to mixed models for familial data.     

Statistical modeling alongside observational data predicts long-term immunogenicity of one dose and two doses of pediatric hepatitis A vaccine in the Mendoza province of Argentina

BackgroundFollow-up for anti-hepatitis A (HA) antibody persistence up to 10 years was conducted after implementation of universal vaccination against HA virus (HAV) in Mendoza, Argentina. Based on these data, statistical modeling was used to predict the antibody persistence to 30 years.MethodsA non-interventional study evaluated long-term immunogenicity (geometric mean concentrations [GMCs] and seroprotection rate) following routine vaccination with 1 dose (Group 1: N = 436) or 2 doses (Group 2: N = 108) of HA vaccine. Associated statistical modeling based on a Bayesian approach of mixed effects models on log transformed titers evaluated three models (linear, piecewise linear, and exponential decay, with and without a natural boosting effect).ResultsFrom the initial cohort, 9 participants (Group 1) and 1 participant (Group 2) showed antibody titers below the seroprotective threshold and received a booster. At Year 10, 190 (Group 1) and 51 (Group 2) participants remained in the study without a booster dose and all were seroprotected. Regarding statistical modeling, the piecewise linear model showed the best fit and demonstrated high and similar seroprotection for each schedule up to 30 years (89% [1-dose schedule], 85% [2-dose schedule]). The 2-dose schedule showed higher GMC (95% CI) than the 1-dose schedule (Year 10: 352 [271–456] versus 78 [69.8–87.6] mIU/mL) and Year 30 (predicted) (37 [13–97] versus 19 [11–34] mIU/mL). Natural boosting had little impact on predicted seroprotection rates at 30 years for the 1-dose schedule (89% [0.8–0.96] and 84% [0.73–0.94] with and without a natural booster, respectively).ConclusionsLong-term persistence of anti-HAV antibodies was observed up to 10 years with 1-dose and 2–dose vaccine schedules, supporting booster flexibility. Statistical modeling predicted good persistence of seroprotection for each schedule up to 30 years. Natural boosting had a limited impact on seroprotection rate predictions, enabling extrapolation of these results to non-endemic settings for traveler vaccination.     

Extended information criterion (EIC) approach for linear mixed effects models under restricted maximum likelihood (REML) estimation

In clinical data analysis, the restricted maximum likelihood (REML) method has been commonly used for estimating variance components in the linear mixed effects model. Under the REML estimation, however, it is not straightforward to compare several linear mixed effects models with different mean and covariance structures. In particular, few approaches have been proposed for the comparison of linear mixed effects models with different mean structures under the REML estimation. We propose an approach using extended information criterion (EIC), which is a bootstrap-based extension of AIC, for comparing linear mixed effects models with different mean and covariance structures under the REML estimation. We present simulation studies and applications to two actual clinical data sets.     

工作场所空气中吡啶的混合溶剂解吸气相色谱测定法

目的建立混合溶剂解吸气相色谱法测定工作场所空气中吡啶的方法。方法工作场所空气中吡啶用碱性活性炭管采集,混合溶剂[10%（体积分数）甲醇二氯甲烷溶液]解吸后进样,经FFAP石英毛细管柱分离,氢焰离子化检测器检测。结果用混合溶剂代替二氯甲烷作解吸溶液,吡啶解吸效率由49.4%提高至82.0%。吡啶浓度在0～60μg/ml范围内线性关系良好,线性方程为：Y=4.31＋8.24 X（r=0.999 9）。检出限为0.07μg/ml,若采集1.5 L空气样品,则最低检出浓度为0.05 mg/m^3,平均加标回收率为98.3%～102.2%,平均解吸效率在81.1%～81.9%,相对标准偏差在0.9%～2.5%。结论该方法适用于工作场所空气中吡啶浓度的测定。     

混合痔结扎加后位扩肛术的临床应用

目的探讨混合痔结扎加后位扩肛术的临床应用的效果。方法回顾性分析北京北郊肿瘤医院收住的500例混合痔病人的资料,选择的病例以中华外科协会肛肠分会,痔诊的暂行标准制定的诊断标准为依据。结果500例患者2周内100%治愈,术后7 a随访无复发无并发症。结论混合痔结扎加后位扩肛术疗效良好。     

Tutorial in biostatistics: spline smoothing with linear mixed models

The semi-parametric regression achieved via penalized spline smoothing can be expressed in a linear mixed models framework. This allows such models to be fitted using standard mixed models software routines with which many biostatisticians are familiar. Moreover, the analysis of complex correlated data structures that are a hallmark of biostatistics, and which are typically analysed using mixed models, can now incorporate directly smoothing of the relationship between an outcome and covariates. In this paper we provide an introduction to both linear mixed models and penalized spline smoothing, and describe the connection between the two. This is illustrated with three examples, the first using birth data from the U.K., the second relating mammographic density to age in a study of female twin-pairs and the third modelling the relationship between age and bronchial hyperresponsiveness in families. The models are fitted in R (a clone of S-plus) and using Markov chain Monte Carlo (MCMC) implemented in the package WinBUGS.     

An autoregressive linear mixed effects model for the analysis of longitudinal data which show profiles approaching asymptotes

In longitudinal data, a continuous response sometimes shows a profile approaching an asymptote. For such data, we propose a new class of models, autoregressive linear mixed effects models in which the current response is regressed on the previous response, fixed effects, and random effects. Asymptotes can shift depending on treatment groups, individuals, and so on, and can be modelled by fixed and random effects. We also propose error structures that are useful in practice. The estimation methods of linear mixed effects models can be used as long as there is no intermittent missing.     

Measuring explained variation in linear mixed effects models

We generalize the well-known R(2) measure for linear regression to linear mixed effects models. Our work was motivated by a cluster-randomized study conducted by the Eastern Cooperative Oncology Group, to compare two different versions of informed consent document. We quantify the variation in the response that is explained by the covariates under the linear mixed model, and study three types of measures to estimate such quantities. The first type of measures make direct use of the estimated variances; the second type of measures use residual sums of squares in analogy to the linear regression; the third type of measures are based on the Kullback-Leibler information gain. All the measures can be easily obtained from software programs that fit linear mixed models. We study the performance of the measures through Monte Carlo simulations, and illustrate the usefulness of the measures on data sets.     

Estimation of genetic and environmental factors for binary traits using family data

While the family-based analysis of genetic and environmental contributions to continuous or Gaussian traits is now straightforward using the linear mixed models approach, the corresponding analysis of complex binary traits is still rather limited. In the latter we usually rely on twin studies or pairs of relatives, but these studies often have limited sample size or have difficulties in dealing with the dependence between the pairs. Direct analysis of extended family data can potentially overcome these limitations. In this paper, we will describe various genetic models that can be analysed using an extended family structure. We use the generalized linear mixed model to deal with the family structure and likelihood-based methodology for parameter inference. The method is completely general, accommodating arbitrary family structures and incomplete data. We illustrate the methodology in great detail using the Swedish birth registry data on pre-eclampsia, a hypertensive condition induced by pregnancy. The statistical challenges include the specification of sensible models that contain a relatively large number of variance components compared to standard mixed models. In our illustration the models will account for maternal or foetal genetic effects, environmental effects, or a combination of these and we show how these effects can be readily estimated using family data.     

Real longitudinal data analysis for real people: Building a good enough mixed model

Mixed effects models have become very popular, especially for the analysis of longitudinal data. One challenge is how to build a good enough mixed effects model. In this paper, we suggest a systematic strategy for addressing this challenge and introduce easily implemented practical advice to build mixed effects models. A general discussion of the scientific strategies motivates the recommended five‐step procedure for model fitting. The need to model both the mean structure (the fixed effects) and the covariance structure (the random effects and residual error) creates the fundamental flexibility and complexity. Some very practical recommendations help to conquer the complexity. Centering, scaling, and full‐rank coding of all the predictor variables radically improve the chances of convergence, computing speed, and numerical accuracy. Applying computational and assumption diagnostics from univariate linear models to mixed model data greatly helps to detect and solve the related computational problems. Applying computational and assumption diagnostics from the univariate linear models to the mixed model data can radically improve the chances of convergence, computing speed, and numerical accuracy. The approach helps to fit more general covariance models, a crucial step in selecting a credible covariance model needed for defensible inference. A detailed demonstration of the recommended strategy is based on data from a published study of a randomized trial of a multicomponent intervention to prevent young adolescents' alcohol use. The discussion highlights a need for additional covariance and inference tools for mixed models. The discussion also highlights the need for improving how scientists and statisticians teach and review the process of finding a good enough mixed model. Copyright © 2009 John Wiley & Sons, Ltd.