SPSS对有序分类资料的统计分析方法

目的 本科及以下,乃至部分研究生使用的《卫生统计学》、《医学统计学》教材和所有有关SPSS的书籍中,没有介绍有序分类资料这一基本的统计分析方法,导致误用无序分类资料的卡方检验方法屡有发生.本文提出利用SPSS卡方检验处理有序分类资料的简易统计分析方法.方法 用SPSS交叉表统计分析方法,选择“线性和线性组合”行的结果作为判别单向和双向有序分类资料的统计量,并用经典的Ridit分析和SAS程序分析结果比较.结果 在SPSS交叉表对单向有序分类资料的实例分析中, “线性和线性组合”的P值(0.022)与Ridit分析和SAS程序统计分析的结果(0.025 8)相近,统计推断结论一致.在双向有序分类资料中, “线性和线性组合”的P值(0.044)与Ridit分析和SAS程序统计分析的结果(0.0446)完全一致.2例均与用无序分类资料的统计分析结果相差很远.结论 “线性和线性组合”对单向和双向有序分类资料均有效；区分有序分类资料与无序资料的统计分析方法,其分析结果和统计推断结论明显不同.建议在各种统计学教材和有关SPSS的书籍中增加这部分内容,并明确提示为有序分类资料的统计分析方法.     

单向有序二维表的统计分析方法比较

目的比较单向有序表的三种统计检验方法的功效。方法采用Monte Carlo模拟。结果模拟结果显示CMH法和K—W秩和检验对分布位置差异的检验有较高的功效，而对分布离散程度的变异却无能为力。结论分析单向有序表时应同时使用Pearson x^2和K—W秩和检验（或CMH法），并报告两种检验的统计结果。     

行×列表资料统计分析方法

行×列表资料可分为单向有序、双向无序、双向有序属性相同和双向有序属性不同四类。 1．单向有序行×列表资料有两种形式：一种是行×列表中的试验分组变量（如药物浓度）是有序的,而指标变量（如染色体损伤类型）是无序的,此单向有序行×列表资料可用行×列表资料的χ^2检验进行分析。另一种情况是行×列表资料中的试验分组变量（如治疗方式）为无序的,而指标变量（如疗效按等级分组）是有序的,此种单向有序行×列表资料的统计处理,应用秩和检验进行分析。     

行×列表资料统计分析方法

行×列表资料可分为单向有序、双向无序、双向有序属性相同和双向有序属性不同四类。 1．单向有序行×列表资料有两种形式：一种是行×列表中的试验分组变量（如药物浓度）是有序的,而指标变量（如染色体损伤类型）是无序的,此单向有序行×列表资料可用行×列表资料的χ^2检验进行分析。     

行×列表资料统计分析方法

行×列表资料可分为单向有序、双向无序、双向有序属性相同和双向有序属性不同四类。1．单向有序行×列表资料有两种形式：一种是行×列表中的试验分组变量（如药物浓度）是有序的，而指标变量（如染色体损伤类型）是无序的，此单向有序行×列表资料可用行×列表资料的）X2检验进行分析。另一种情况是行×列表资料中的试验分组变量（如治疗方式）为无序的，     

具有相关关系的二分类资料处理方法比较

目的探讨分析具有相关关系的二分类资料的有效处理方法。方法采用蒙特卡罗模拟比较广义估计方程和广义随机效应模型与一般logistic回归在处理具有相关关系的二分类资料的区别。结果一般logisitc回归处理相关关系的二分类资料时假阳性率增加。广义估计方程与广义随机效应模型是处理该类型资料时,I类错误能稳定控制在0.05左右,且检验效能基本一致。结论广义估计方程和广义随机效应模型是处理具有相关关系的二分类资料的合适方法,不能采用一般logistic回归代替。     

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。     

2015年中国成年人自报癌症患者分布特征与危险因素分析

目的:分析2015年中国成年人自报癌症患者分布特征及其危险因素。方法:使用2015年中国慢性病及其危险因素监测数据,通过描述性统计分析分类变量的频率和比例,采用 χ2检验比较无序分类变量,采用单因素logistic回归和多因素logistic回归筛选出癌症患者可能的危险因素。 结果:2015年中国...     

CMH统计分析方法在多中心试验s×r表资料的应用

多中心试验中,有时试验的对象分为两组或两组以上,分别给予不同的处理,其结果作为反应变量不仅仅是二分类,也可能是多分类,且属性可能是有序的,也可能是无序的或名义的,这样构成分层的ｓ×ｒ列联表资料。多中心试验的２×２、２×ｒ（列变量为有序变量）和ｓ×２（...     

倾向得分区间匹配法用于非随机对照试验的探索与研究

目的探索研究倾向得分区间匹配法在非随机对照试验中用于均衡组间混杂因素的能力,并与logistic回归分析方法和倾向得分卡钳匹配进行比较。方法通过Monte Carlo模拟分析倾向得分区间匹配法处理二分类资料的能力,并与传统的logistic回归方法以及倾向得分卡钳匹配法进行比较,通过I类错误、检验效能、标准化差异以及匹配比例等指标进行综合评价。结果倾向得分区间匹配法与logistic回归法以及倾向得分卡钳匹配法的检验效能、I类错误、标准化差异和匹配比例四个评价指标无明显差异。结论在观察性研究和流行病学研究中,采用倾向得分区间匹配法均衡组间协变量得到真实的处理效应具有很高的实用价值。     

Analysis of mixed categorical data in 2 x K contingency tables

2 x K contingency tables having both ordinal and nominal categories are often encountered in various types of studies. Such data are referred to as 'mixed' categorical data in this article. To apply a method for ordered categorical data one has to discard the nominal categories, and to apply a method for nominal categories one has to discard the ordering information inherent in the ordered categories. Therefore, investigators often either discard observations in nominal categories or discard the ordering of the categories before analysing such data. Some information will be lost in both approaches. A method for analysing data in 2 x K 'mixed' tables is proposed in this paper which can be considered as an extension of well known methods for nominal and ordered categories. The proposed method utilizes observations in the nominal categories as well as the ordering information. If all the categories were ordered then the proposed method reduces to the trend test, and if all the categories were nominal then the proposed method reduced to Pearson's chi-square test.     

Robustness and power of analysis of covariance applied to ordinal scaled data as arising in randomized controlled trials

In clinical trials comparing two treatments, ordinal scales of three, four or five points are often used to assess severity, both prior to and after treatment. Analysis of covariance is an attractive technique, however, the data clearly violate the normality assumption and in the presence of small samples, and large sample theory may not apply. The robustness and power of various versions of parametric analysis of covariance applied to small samples of ordinal scaled data are investigated through computer simulation. Subjects are randomized to one of two competing treatments and the pre-treatment, or baseline, assessment is used as the covariate. We compare two parametric analysis of covariance tests that vary according to the treatment of the homogeneity of regressions slopes and the two independent samples t-test on difference scores. Under the null hypothesis of no difference in adjusted treatment means, we estimated actual significance levels by comparing observed test statistics to appropriate critical values from the F- and t-distributions for nominal significance levels of 0.10, 0.05, 0.02 and 0.01. We estimated power by similar comparisons under various alternative hypotheses. The model which assumes homogeneous slopes and the t-test on difference scores were robust in the presence of three, four and five point ordinal scales. The hierarchical approach which first tests for homogeneity of regression slopes and then fits separate slopes if there is significant non-homogeneity produced significance levels that exceeded the nominal levels especially when the sample sizes were small. The model which assumes homogeneous regression slopes produced the highest power among competing tests for all of the configurations investigated. The t-test on difference scores also produced good power in the presence of small samples.     

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。     

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.     

Testing logistic regression coefficients with clustered data and few positive outcomes

Applications frequently involve logistic regression analysis with clustered data where there are few positive outcomes in some of the independent variable categories. For example, an application is given here that analyzes the association of asthma with various demographic variables and risk factors using data from the third National Health and Nutrition Examination Survey, a weighted multi stage cluster sample. Although there are 742 asthma cases in all (out of 18,395 individuals), for one of the categories of one of the independent variables there are only 25 asthma cases (out of 695 individuals). Generalized Wald and score hypothesis tests, which use appropriate cluster-level variance estimators, and a bootstrap hypothesis test have been proposed for testing logistic regression coefficients with cluster samples. When there are few positive outcomes, simulations presented in this paper show that these tests can sometimes have either inflated or very conservative levels. A simulation-based method is proposed for testing logistic regression coefficients with cluster samples when there are few positive outcomes. This testing methodology is shown to compare favorably with the generalized Wald and score tests and the bootstrap hypothesis test in terms of maintaining nominal levels. The proposed method is also useful when testing goodness-of-fit of logistic regression models using deciles-of-risk tables.     

Fast, exact linkage analysis for categorical traits on arbitrary pedigree designs

Multi-symptom diseases without a consistent continuous measurement of severity may be best understood with a categorical interpretation. In this paper, we present LOCate v.2, a fast, exact algorithm for linkage analysis of all types of categorical traits, both ordinal and nominal. Our method is able to incorporate missing data and analyze complex genealogical structure, including inbreeding loops. LOCate v.2 computes exact likelihoods efficiently through an elimination algorithm, similar to that used by Superlink for binary traits. We compare LOCate v.2 to LOT and QTLlink, two existing methods of linkage analysis for ordinal traits. We find that LOCate v.2 outperforms both methods when used to analyze simulated nominal traits. In addition, LOCate v.2 performs as well as QTLlink on simulated ordinal traits, and better than LOT due to the necessity of cutting large pedigrees for analysis in LOT. To demonstrate the versatility of LOCate v.2, we conduct an ordinal and nominal linkage analysis of ventricular arrhythmias in a large, inbred pedigree of German Shepherd dogs. We find that a trichotomous ordinal or nominal interpretation strengthens the evidence in favor of linkage to a region on chromosome 6, and provides new evidence of linkage to a region on chromosome 11. LOCate v.2 is a unified, fast, and robust method for linkage analysis of ordinal and nominal traits which will be valuable to researchers interested in investigating any type of categorical trait.     

多分类有序反应变量logistic回归应用条件的检验

目的 对多分类有序反应变量logistic回归的应用条件寻求科学合理的检验方法。方法 使用卡方分布的理论，SAS软件及抽样调查方法。结果 设计出多分类有序反应变量logistic回归应用条件的卡方检验方法，推导出反应变量取各水平的概率计算公式及卡方检验中理论值、自由度的计算公式，并在作者主持的国家医师资格临床实践技能考试研究中取得了成功效果。结论 多分类有序反应变量logistic回归得到完善和补充，具有较大的理论和实际意义。     

A mixed-effects multinomial logistic regression model

A mixed-effects multinomial logistic regression model is described for analysis of clustered or longitudinal nominal or ordinal response data. The model is parameterized to allow flexibility in the choice of contrasts used to represent comparisons across the response categories. Estimation is achieved using a maximum marginal likelihood (MML) solution that uses quadrature to numerically integrate over the distribution of random effects. An analysis of a psychiatric data set, in which homeless adults with serious mental illness are repeatedly classified in terms of their living arrangement, is used to illustrate features of the model.     

Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout

The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose 2 approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation technique for the sequential logistic regression and a parameter‐expanded monotone data augmentation scheme for the multivariate probit model. We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods.