我国恶性肿瘤监测框架初探

目的:探讨建立一个地理分布相对均匀,地区分类相对合理的分类体系,以便进行恶性肿瘤的长期监测,为肿瘤防治提供依据.方法:利用1973～1975年全国人口死因回顾调查中位于前9位的恶性肿瘤调整死亡率资料,根据拟定的3条判断准则对6个二维有序样品条件系统聚类方法的聚类结果进行选择.结果:通过二维有序样品条件系统聚类方法中的最长距离法把全国30个省、市、自治区分成8类,并在每一类内设置一个监测点.结论:加入条件约束后,最长距离法和离差平方和法的聚类结果被认为是较合理和均匀的.     

一维有序样品的条件系统聚类

目的：介绍并讨论一种适用于一维有序样品的条件系统聚类方法。方法：对有序样品,首先构造条件距阵,然后把条件矩阵和距离矩阵相结合,构成条件距离矩阵,并采用系统聚类的思维方法进行聚类,用该方法对一组数据进行聚类,还进行了模拟研究,并与最优分割法作了比较。结果,该方法操作简单,计算方便,并能够得到较稳定的聚类结果。结论：一维有序样品的条件系统聚类法理论朴素,实践方便,是一种较好的可用于有序样品聚类的方法。     

关于数理统计中系统聚类法的讨论

目的 研究聚类分析中系统聚类法的某些聚类过程对聚类结果产生的干扰，寻找消除该干扰的聚类过程。方法 利用图论和模糊数学中的最大树聚类法为标准，对不同的聚类过程进行分析，找出系统聚类法中某些聚类过程给聚类结果带来的严重影响的原因。结果给出能消除系统聚类法中某些聚类过程给聚类结果带来严重影响的统一的（指标或样品）聚类过程。结论 统一的（指标或样品）聚类过程消除了系统聚类法中某些聚类过程给聚类结果带来的严重影响；不但保留了系统聚类法中聚类过程的优点，而且还能挖掘出隐藏在原始数据中的有用信息。     

卫生统计

021556_维有序样品的条件系统聚类/沈;毅··://中国卫生统计..2001,’18(1).一6～8 ‘ 介绍并讨论一种适用于一维有序样品的条件系统聚类方法。对有序样品,首先构造条件矩阵,然后把条件矩阵和距离矩阵相结合,构成条件距离矩阵,并采用系统聚类的思维方法进行聚类。用该方法对一     

系统聚类法与综合指数法在临床科室评先中的应用

目的找到一种使医院不同业务性质临床科室能够进行评比的标准尺度。方法采用系统聚类法与综合指数法相结合的评价方法,用系统聚类法将临床科室依据工作指标进行分类,再用综合指数法在每一类临床科室中选出最优科室。结果实现了依据多数量指标聚类选最优临床科室。结论通过系统聚类法与综合指数法的结合,实现医院不同性质临床科室之间的评比,克服了主观人为因素的影响,为医院激励优秀科室提供了参考方法。     

动态聚类分析在中医方剂药量组合规律中的应用

聚类分析（cluster analysis）又称为集群分析、点群分析、簇分析等。该多变量分析方法根据实际的需要和聚类对象是样品还是变量，可分为两种类型，一种是对样品聚类（称为Q-型聚类），另一种是对变量聚类（称为R-型聚类）。在数学上，又可根据不同的聚类思想和策略，分为系统聚类（hierarchical clustering method）和非系统聚类（nonhierarchical clusteringmethod）两大类；前者主要分为集结法（agglomerative method）及分解法（divisive method）。对于样品聚类，当聚类对象很多时，若采用系统聚类法，则计算量很大，统计软件也需要计算很长时间，而且作出的聚类图（SPSS软件中为Dendrogram图或Icicle图）很复杂，难以解释，这时，我们可采用动态聚类法，也称为快速聚类法（quick cluster）来实现。本研究应用快速聚类法，利用SPSS12．0软件中K-means Cluster过程，对用于治疗慢性胃炎的汉代著名方剂半夏泻心汤的临床用药量进行了分析。     

灰色聚类在医院综合评价中的应用研究

本文应用灰色聚类方法对医院进行综合评价,并对标定聚类权进行了研究。要根据参加综合评价的指标作用情况不同,采用不同的计算方法计算标定聚类权,这是评价结果是否科学的关键问题。一般说来,不同类型指标在综合评价中作用大小是不一样的,为此,本文认为采用加权法计算标定聚类权,使判断结果更科学。     

基于系统聚类与密切值法的临床科室综合效益评价

目的应用系统聚类和密切值法评价临床科室效益,为医院管理临床科室综合评价问题提供方法。方法用系统聚类法将各临床科室分类、密切值法对各临床科室综合效益进行评价并排序。结果系统聚类为4类,结合密切值排序依次为:1类儿科;2类妇产科、消化内科、呼吸内科、神经内科、普外科、肾病内科、心血管内科、眼科、耳鼻咽喉科;3类骨科、感染疾病科、神经外科、肝胆外科、胸外科、ICU;4类康复科。结论系统聚类法和密切值法的结合运用可以准确对医院临床科室综合效益进行分类和评价,结果直观、可靠。     

可拓聚类方法在医院年收治病人数预测中的应用

目的 将可拓聚类方法用于对医院年收治病人数的预测.方法 构造可拓聚类预测模型,判断待聚类样本所属类别,对其进行预测.结果 利用1981~1989年的相关因素数据资料构造可拓聚类预测模型,对1990年的收治病人数进行预测,结果与实际相符.结论 可拓聚类方法用于对医院年收治病人数的预测是有效可行的,可以为医院管理与统计提供一种新的方法.     

计划免疫绩效灰关联评价方法研究

对多因素的复杂系统进行综合评估分析,已作了大量的理论研究和实践探索。如因素关联分析,模糊聚类,系统聚类,灰色聚类等。而本文把灰关联分析和聚类思想方法进行融汇、扩充,创立了“灰关联聚类方法”,既区别于关联分析,又非是一般的聚类方法,它把灰关联度演化成刻划待评对象之间     

Three methods for analysing correlated ROC curves: a comparison in real data sets from multi-reader,multi-case studies with a factorial design

This paper compares three published methods for analysing multiple correlated ROC curves: a method using generalized estimating equations with marginal non-proportional ordinal regression models; a method using jackknifed pseudovalues of summary statistics; a method using a corrected F-test from analysis of variance of summary statistics. Use of these methods is illustrated through six real data examples from studies with the common factorial design, that is, multiple readers interpreting images obtained with each test modality on each study subject. The issue of the difference between typical summary statistics and summary statistics from typical ROC curves is explored. The examples also address similarities and differences among the analytical methods. In particular, while point estimates of differences between test modalities are similar, the standard errors of these differences do not agree for all three methods. A simulation study supports the standard errors provided by the generalized estimating equations with marginal non-proportional ordinal regression models.     

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.     

OPTIMAL DISCRIMINANT ANALYSIS FOR ORDINAL RESPONSES

Optimal classification formulation is adapted to the context of discrimination when the response is ordinal. The resulting method, optimal discriminant analysis for ordinal responses (ODAO), is presented and compared with two reference discrimination techniques used in this context (proportional-odds ordinal logistic regression and normal discrimination) using a study of prognosis following burn injuries and simulated data. The ODAO method clearly outperforms both reference methods in terms of classification accuracy (in training and validation samples), robustness to outliers, simplicity of use and applicability in clinical settings. ODAO is a promising method for improving classification performance in discrimination with ordinal responses and merits further investigation. © 1997 by John Wiley & Sons, Ltd.     

Ordinal response prediction using bootstrap aggregation,with application to a high‐throughput methylation data set

Many investigators conducting translational research are performing high‐throughput genomic experiments and then developing multigenic classifiers using the resulting high‐dimensional data set. In a large number of applications, the class to be predicted may be inherently ordinal. Examples of ordinal outcomes include tumor‐node‐metastasis (TNM) stage (I, II, III, IV); drug toxicity evaluated as none, mild, moderate, or severe; and response to treatment classified as complete response, partial response, stable disease, or progressive disease. While one can apply nominal response classification methods to ordinal response data, in doing so some information is lost that may improve the predictive performance of the classifier. This study examined the effectiveness of alternative ordinal splitting functions combined with bootstrap aggregation for classifying an ordinal response. We demonstrate that the ordinal impurity and ordered twoing methods have desirable properties for classifying ordinal response data and both perform well in comparison to other previously described methods. Developing a multigenic classifier is a common goal for microarray studies, and therefore application of the ordinal ensemble methods is demonstrated on a high‐throughput methylation data set. Copyright © 2009 John Wiley & Sons, Ltd.     

A comparison of methods for correlated ordinal measures with ophthalmic applications

For many clinical trials and epidemiologic investigations in the field of ophthalmology, paired ordinal data are often collected through the detailed grading of retinal photographs. One method for analysis of these data is the extension of the generalized estimating equation (GEE) methodology to multinomial data with cumulative link functions. Prior to the development of this advanced technique, however, ophthalmologists developed a method of combining the ordinal responses of both eyes of a patient into a single person-level response on a new ordinal scale. A relationship between the regression coefficients of these two methods is derived as a function of the correlation between eyes. We investigate the applicability of this result and the relationship of the standard errors in simulation experiments and in an example from the Wisconsin Epidemiologic Study of Diabetic Retinopathy.     

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 novel modeling framework for ordinal data defined by collapsed counts

Adolescent alcohol use is a serious public health concern. Despite advances in the theoretical conceptualization of pathways to alcohol use, researchers are limited by the statistical techniques currently available. Researchers often fit linear models and restrictive categorical models (e.g., proportional odds models) to ordinal data with many response categories defined by collapsed count data (0 drinking days, 1–2days, 3–6days, etc.). Consequently, existing models ignore the underlying count process, resulting in disjoint between the construct of interest and the models being fitted. Our proposed ordinal modeling approach overcomes this limitation by explicitly linking ordinal responses to a suitable underlying count distribution. In doing so, researchers can use maximum likelihood estimation to fit count models to ordinal data as if they had directly observed the underlying discrete counts. The usefulness of our ordinal negative binomial and ordinal zero‐inflated negative binomial models is verified by simulation studies. We also demonstrate our approach using real empirical data from the 2010 National Survey of Drug Use and Health. Results show the benefit of the proposed ordinal modeling framework compared with existing methods. Copyright © 2015 John Wiley & Sons, Ltd.     

Univariate analysis of dichotomous or ordinal data from twin pairs: A simulation study comparing structural equation modeling and logistic regression

The univariate analysis of categorical twin data can be performed using either structural equation modeling (SEM) or logistic regression. This paper presents a comparison between these two methods using a simulation study. Dichotomous and ordinal (three category) twin data are simulated under two different sample sizes (1,000 and 2,000 twin pairs) and according to different additive genetic and common environmental models of phenotypic variation. The two methods are found to be generally comparable in their ability to detect a “correct” model under the specifications of the simulation. Both methods lack power to detect the right model for dichotomous data when the additive genetic effect is low (between 10 and 20%) or medium (between 30 and 40%); the ordinal data simulations produce similar results except for the additive genetic model with medium or high heritability. Neither method could adequately detect a correct model that included a modest common environmental effect (20%) even when the additive genetic effect was large and the sample size included 2,000 twin pairs. The SEM method was found to have better power than logistic regression when there is a medium (30%) or high (50%) additive genetic effect and a modest common environmental effect. Conversely, logistic regression performed better than SEM in correctly detecting additive genetic effects with simulated ordinal data (for both 1,000 and 2,000 pairs) that did not contain modest common environmental effects; in this case the SEM method incorrectly detected a common environmental effect that was not present. © 1996 Wiley-Liss, Inc.