临床试验中分类数据的处理方法比较

目的通过模拟比较,探讨分析多中心临床试验中高维列联表资料的有效方法。方法采用Monte Carlo模拟比较CMH检验与logistic回归处理多分类单向有序、无序资料以及CMH检验与一般线性模型GLM(General Linear Model)处理双向有序资料的区别。结果多分类单向有序和无序资料采用CMH检验和logistic回归分析结果相同,检验效能基本一致。双向有序资料采用GLM与CMH检验分析结果相同,检验效能基本一致。结论在多中心临床试验中,高维列联表资料的统计分析可以用logistic回归或GLM代替CMH卡方检验。     

改进累积logistic回归在生存质量研究中的应用

目的通常多分类有序变量类别之间的差距并不相等,一般的数据分析方法却将其作等距对待,本文探讨有序变量确实存在间距差异时,对累积logistic模型加以修正的方法。方法若经过检验发现有序变量确实存在间距差异,通过引入虚拟工具变量对累积logistic模型加以修正。结果本文实例中有序反应变量存在间距差异,应用改进累积logistic模型,通过以工具虚拟变量的不同取值为参照,使结果的解释更符合实际。结论若经过检验发现有序变量确实存在间距差异,通过对改进累积logistic模型的实际应用,取得良好效果。     

上海市嘉定区农村居民就诊单位选择的影响因素分析--决策树和多分类无序反应变量的logistic回归相结合的方法

目的 分析影响上海市嘉定区农村居民就诊单位选择的因素，并探讨logistic回归分析和决策树(CHAID法)相结合的方法的应用。方法 采用多分类无序反应变量的logistic回归分析和决策树(CHAID法)相结合的方法，用似然比检验比较模型之间的优劣，确定最佳模型。结果 影响嘉定区农村居民选择就诊单位的主要因素是保健制度、年龄、恩格尔系数、性别、看病距离、职业以及保健制度和文化程度的交互作用。结论 决策树提供解释变量类别的重新划分和因素之间的相互作用的信息，logistic模型提供因素的主效应作用以及对交互作用是否存在的检验信息，两者结合，可以更加全面地挖掘出对目标变量有影响的因素及因素之间的交互作用。     

相关二分类反应变量资料的Binomial-Poisson层次模型分析

目的探讨数据存在相关性时,二分类反应变量资料的统计分析方法。方法构建Binomial-Poisson层次模型,并结合毒理学实例与logistic回归模型进行比较。结果随机效应logistic回归模型和Binomial-Poisson层次模型都考虑了数据间的相关性,假设检验结论一致,但与传统logistic回归模型结论相反,而Binomial-poisson层次模型结果更加稳定。结论层次结构模型可有效分析相关二分类反应变量数据资料。     

基于logistic回归的老年人健康状况与认知功能关系研究

目的:了解我国60岁以上老年人认知功能现状,探究健康状况与认知功能之间的关系.方法:选取2015年CHARLS数据库中60岁以上老年人,采用卡方检验比较不同组间是否存在差异;利用二分类logistic回归分析健康状况和认知功能的关系.结果:有效样本6350例.二分类logistic回归分析中发现老年人群中,女性较男性的...     

多个二项反应变量多水平因子分析模型的原理及应用

目的探讨多个二项反应变量多水平因子分析模型的应用。方法在MLwiN2．02软件中，采用马尔科夫链一蒙特卡罗参数估计方法，通过实例模拟及分析，说明模型的实际应用。结果该模型适用于反应变量为二项分类的、具有层次结构的数据。结论多个二项反应变量多水平因子分析模型有其独特的意义和用途。     

四川省富顺县农村老年人营养不良及其影响因素调查

目的探讨农村老年人营养不良状况及其相关因素,为采取防治措施提供参考依据。方法采用分层随机抽样方法,对富顺县60岁及以上老年人进行问卷调查,并选取卡方检验、秩和检验及有序多分类Logistics回归方法进行数据分析。结果 303名农村老年人营养状况的均分为(20.52±2.70)分,老年人营养不良率为9.6%,营养不良风险率为78.5%,营养正常率为11.9%。有序多分类Logistics回归分析显示,经济状况、日常生活能力、认知功能、自评健康状况是营养不良的独立影响因素。结论富顺县农村老年人存在营养不良风险人群比例很高,经济水平差、日常生活能力差、认知障碍以及自评健康状况差是营养不良的危险因素。     

非线性混合效应模型和广义线性模型拟合随机效应logistic回归的应用比较

在临床药物试验中药物疗效的评价经常遇到二分类资料,即反应变量有两个水平如有效、无效;成功、失败等.二分类变量服从二项分布,可采用logistic回归模型. 运用logistic回归模型对分类资料进行分析,能给实际研究带来很多便利.与多元线性回归相比,logistic回归具有许多独特的优点,如对正态性和方差齐性不做要求,系数的可解释性等.     

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。     

睡眠时间与肥胖儿童代谢综合征的关系

目的 探讨睡眠时间与肥胖儿童代谢综合征(MS)的关系.方法 对311例肥胖儿童分别记录睡眠时间及测定空腹血糖(FBG)、血脂、血压、BMI等有关MS的诊断指标,并根据睡眠时间(＜8h、8 h～、9h～、≥10h)分为4组进行比较分析,将MS作为反应变量、睡眠时间作为自变量,正常BMI儿童作为对照,进行多分类无序反应变量的logistic回归分析结果 每天睡眠时间＜8 h的肥胖儿童与每天睡眠时间≥10h的肥胖儿童比较.各项MS组分测定值(BMI、血压、血脂、FBG)差异有统计学意义(P＜0.05),MS及血压、空腹胰岛素、BMI异常(≥30kg/m2)发生率的差异也有统计学意义(P＜0.05);通过多分类无序反应变量的logistic回归分析发现睡眠时间减少是肥胖儿童发生MS的危险因素.结论 睡眠不足是肥胖儿童发生MS的重要影响因素.     

Polytomous logistic regression analysis could be applied more often in diagnostic research

OBJECTIVE: Physicians commonly consider the presence of all differential diagnoses simultaneously. Polytomous logistic regression modeling allows for simultaneous estimation of the probability of multiple diagnoses. We discuss and (empirically) illustrate the value of this method for diagnostic research. STUDY DESIGN AND SETTING: We used data from a study on the diagnosis of residual retroperitoneal mass histology in patients presenting with nonseminomatous testicular germ cell tumor. The differential diagnoses include benign tissue, mature teratoma, and viable cancer. Probabilities of each diagnosis were estimated with a polytomous logistic regression model and compared with the probabilities estimated from two consecutive dichotomous logistic regression models. RESULTS: We provide interpretations of the odds ratios derived from the polytomous regression model and present a simple score chart to facilitate calculation of predicted probabilities from the polytomous model. For both modeling methods, we show the calibration plots and receiver operating characteristics curve (ROC) areas comparing each diagnostic outcome category with the other two. The ROC areas for benign tissue, mature teratoma, and viable cancer were similar for both modeling methods, 0.83 (95% confidence interval [CI]=0.80-0.85) vs. 0.83 (95% CI=0.80-0.85), 0.78 (95% CI=0.75-0.81) vs. 0.78 (95% CI=0.75-0.81), and 0.66 (95% CI=0.61-0.71) vs. 0.64 (95% CI=0.59-0.69), for polytomous and dichotomous regression models, respectively. CONCLUSION: Polytomous logistic regression is a useful technique to simultaneously model predicted probabilities of multiple diagnostic outcome categories. The performance of a polytomous prediction model can be assessed similarly to a dichotomous logistic regression model, and predictions by a polytomous model can be made with a user-friendly method. Because the simultaneous consideration of the presence of multiple (differential) conditions serves clinical practice better than consideration of the presence of only one target condition, polytomous logistic regression could be applied more often in diagnostic research.     

有序结果变量回归及其应用

目的：介绍有序结果变量回归（Ordinal Regression）的基本原理,SPSS10．0的Ordinal Regression过程和确定“最佳”模型的方法。方法：介绍有序结果变量回归模型结构和模型评估指标,用一个实例和SPSS10．0的Ordinal Regression过程,说明有序结果变量回归的操作和结果分析。结果：列出五种连接函数两种模型的各种评估指标和总正确率,“最佳”有序结果变量回归模型的系数表。结果：有序结果变量回归适用于两个或以上分类水平的应变量和一组协变量。通过五种连接函数两种模型的各种评估指标和总正确率的比较,可挑选出“最佳”有序结果变量回归模型,并能预测某个体的分类。     

A goodness‐of‐fit test for the proportional odds regression model

We examine goodness‐of‐fit tests for the proportional odds logistic regression model—the most commonly used regression model for an ordinal response variable. We derive a test statistic based on the Hosmer–Lemeshow test for binary logistic regression. Using a simulation study, we investigate the distribution and power properties of this test and compare these with those of three other goodness‐of‐fit tests. The new test has lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. Moreover, the test allows for the results to be summarized in a contingency table of observed and estimated frequencies, which is a useful supplementary tool to assess model fit. We illustrate the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents. The test proposed in this paper is similar to a recently developed goodness‐of‐fit test for multinomial logistic regression. A unified approach for testing goodness of fit is now available for binary, multinomial, and ordinal logistic regression models. Copyright © 2012 John Wiley & Sons, Ltd.     

多分类 logistic 回归在冠心病危险因素研究中的应用

目的：应用多分类logistic回归找出冠心病危险因素与冠状动脉病变程度间的关系,建立冠状动脉病变程度危险因素的“最优”回归方程。方法采取系统抽样的方法,对来自2005年1月至2009年12月期间山东省潍坊市某几所医院心血管内科初步诊断为冠心病并进行冠脉造影的患者病例,抽取256例作为多分类logistic回归分析的样本。冠心病组根据狭窄病变累及血管范围分为1支病变（单支病变组）、2支病变（双支病变组）和3支及以上病变（多支病变组）。通过多分类logistic回归方法分析冠心病与冠脉病变程度相关关系。结果以冠脉病变程度为因变量,各个因素为自变量。根据单因素分析的结果,从20个研究因素中筛选出8个有统计学意义的影响因素,经过数据相关性分析、共线性诊断及专业解释等,筛选出8个影响因素进入多分类logistic回归中,最后得到与冠脉病变程度有关的有统计学意义的影响因素6个,分别是年龄、合并疾病、心率、血糖、脂蛋白（a）;保护因素1个：X17（载脂蛋白A1）。根据筛选出的6个影响因素建立“最优”回归方程。结论应用多分类logistic回归找出与冠脉病变程度有关的危险因素,并定量分析出各危险因素在冠脉病变不同程度上的概率值。     

A method for using incomplete triads to test maternally mediated genetic effects and parent-of-origin effects in relation to a quantitative trait

The authors recently developed a semiparametric family-based test for linkage and association between markers and quantitative traits. This quantitative polytomous logistic regression test allows for analysis of families with incomplete information on parental genotype. In addition, it is not necessary to assume normality of the quantitative trait. Previous simulations have shown that the new test is as powerful as the other widely used tests for linkage disequilibrium in relation to a quantitative trait. Here the authors propose an extension to quantitative polytomous logistic regression that allows testing for maternally mediated effects and parent-of-origin effects in the same framework. Missing data on parental genotype are accommodated through an expectation-maximization algorithm approach. Simulations show robustness of the new tests and good power for detecting effects in the presence or absence of offspring effects. Methods are illustrated with birth weight and gestational length, two quantitative outcomes for which data were collected in a Montreal, Canada, study of intrauterine growth restriction between May 1998 and June 2000.     

Polytomous regression did not outperform dichotomous logistic regression in diagnosing serious bacterial infections in febrile children

OBJECTIVE: To compare polytomous and dichotomous logistic regression analyses in diagnosing serious bacterial infections (SBIs) in children with fever without apparent source (FWS). STUDY DESIGN AND SETTING: We analyzed data of 595 children aged 1-36 months, who attended the emergency department with fever without source. Outcome categories were SBI, subdivided in pneumonia and other-SBI (OSBI), and non-SBI. Potential predictors were selected based on previous studies and literature. Four models were developed: a polytomous model, estimating probabilities for three diagnostic categories simultaneously; two sequential dichotomous models, which differed in variable selection, discriminating SBI and non-SBI in step 1, and pneumonia and OSBI in step 2; and model 4, where each outcome category was opposed to the other two. The models were compared with respect to the area under the receiver-operating characteristic curve (AUC) for each of the three outcome categories and to the variable selection. RESULTS: Small differences were found in the variables that were selected in the polytomous and dichotomous models. The AUCs of the three outcome categories were similar for each modeling strategy. CONCLUSION: A polytomous logistic regression analysis did not outperform sequential and single application of dichotomous logistic regression analyses in diagnosing SBIs in children with FWS.     

Logistic regression when binary predictor variables are highly correlated

Standard logistic regression can produce estimates having large mean square error when predictor variables are multicollinear. Ridge regression and principal components regression can reduce the impact of multicollinearity in ordinary least squares regression. Generalizations of these, applicable in the logistic regression framework, are alternatives to standard logistic regression. It is shown that estimates obtained via ridge and principal components logistic regression can have smaller mean square error than estimates obtained through standard logistic regression. Recommendations for choosing among standard, ridge and principal components logistic regression are developed. Published in 2001 by John Wiley & Sons, Ltd.     

Regression with an ordered categorical response

A survey on Mseleni joint disease in South Africa involved the scoring of pelvic X-rays of women to measure osteoporosis. The scores were ordinal by construction and ranged from 0 to 12. It is standard practice to use ordinary regression techniques with an ordinal response that has that many categories. We give evidence for these data that the constraints on the response result in a misleading regression analysis. McCullagh's proportional-odds model is designed specifically for the regression analysis of ordinal data. We demonstrate the technique on these data, and show how it fills the gap between ordinary regression and logistic regression (for discrete data with two categories). In addition, we demonstrate non-parametric versions of these models that do not make any linearity assumptions about the regression function.     

医学诊断试验评价的ROC分析—重复测量诊断数据的ROC曲线

目的 探讨诊断试验中重复有序分类测量数据的ROC分析方法。方法 在有序模型基础上构造相应的ROC模型,然后选用累积logistic模型作为联系函数,采用GEE方法进行参数估计。结果 通过累积logistic模型的参数估计,能够得到ROC曲线方程、ROC曲线下面积及标准误。结论 上术方法影像诊断试验中常见的重复测量数据进行有效地分析与处理。