单向有序列联表资料统计处理的正确方法

单向有序列联表资料在临床医学试验中很常见,常被当作一般的列联表资料而进行χ2检验。由于损失了疗效所包含的有序(等级)信息,使得统计结果有时不能正确反映临床试验结果。对这类资料,正确的统计分析方法是非参数统计的秩和检验。     

统计分析中检验方法的选择

1.一组样本资料 若来自正态总体,可用t检验;若来自非正态总体或总体分布无法确定,可用Wilcoxon符号秩和检验。 2.配对设计资料 二分类变量,可用McNemar检验;有序多分类变量,可用Wilcoxon符号秩和检验;连续型变量,若来自正态总体,可用配对t检验,否则可用Wilcoxon符号秩和检验。     

应用SAS软件实现毒理学资料非参数检验统计

毒理学研究中的许多资料的统计不适宜采用参数检验，如等级资料、非正态分布或虽呈正态分布但方差不齐，对这些资料的推断性统计，除了部分资料进行数据转换后可进行参数检验外，很大一部分资料需进行非参数检验。许多统计软件都提供了非参数检验的程序，如SAS统计软件的NPARlWAY过程，但如需进一步作多样本资料的两两比较或各染毒组与对照组比较，则没有直接的程序，需进行复杂的编程才能完成。现介绍非参数检验的Wilcoxon—Wilcox检验及多样本两两比较的SAS统计程序。     

统计分析中检验方法的选择

1．一组样本资料若来自正态总体,可用t检验;若来自非正态总体或总体分布无法确定,可用Wilcoxon符号秩和检验。 2．配对设计资料二分类变量,可用McNemar检验;有序多分类变量,可用Wilcoxon符号秩和检验;连续型变量,若来自正态总体,可用配对t检验,否则可用Wilcoxon符号秩和检验。     

对SPSS统计软件中Mcnemar检验结果的讨论

目的比较手工计算与SPSS软件计算Mcnemar检验的结果,分析结果不一样的原因。方法选取相关书籍上配对设计资料的应用实例,用两种方法计算:一是利用Mcnemar检验公式进行手工计算,二是用SPSS统计软件计算,比较两种计算方法的结果有何不同。结果手工计算的结果为P=0.001,SPSS软件计算的结果为P=0.002,两种方法的结果有出入。结论 SPSS统计软件对配对设计四格表资料进行计算时采用的是一种精确检验,运用的是二项分布原理,虽然与手工计算的结果有些出入,但无论用哪种方法都不会影响到最终的统计推断。     

医疗器械软件概述

目的全面深入地了解医疗器械软件的定义和分类、重要标准、行业发展。方法通过比较国内和国外医疗器械软件发展情况,着重论述国内医疗器械软件在标准制定、产业发展和监管等方面的措施。结果与结论我国应在医疗器械软件管理、技术标准制修订方面进行深入的研究和探讨,积极参与国际交流,及时了解国际标准化发展趋势和动态,尽快建立起中国的医疗器械软件国际标准体系。     

应用VBA语言实现完全随机设计多组差别的秩和检验

在毒理学研究和应用领域，完全随机设计多组差别的秩和检验(特别是单向有序列联表资料的秩和检验)方法应用广泛。但是此检验过程非常复杂，计算量大，尤其是混合编秩号步骤计算繁琐、容易出错。而一些统计软件却没有完整地提供此分析模块，如SPSS 10．0只提供了Kruskal-Wallis法，只能做总体分析，不     

应用Excel软件函数分析护理实验设计中的x2检验

目的:本文通过实例简要介绍利用Excel软件统计函数CHITEST、CHITNV和CHIDIST功能可实现固定格式化x2检验,且一旦格式固定,可以很方便的应用于护理研究设计中的定性资料.该方法操作简便,结果直观,非常适合在护理科研中推广应用.     

药品检验网络管理软件的设计与应用

药品检验所药品管理软件很少 ,本所使用Ｗｉｎｄｏｗｓ95联网后 ,笔者根据本所实际 ,编写了药品检验网络管理软件。它包括业务室检品管理和各科室检品管理两大部分 ,在业务室和各科室之间各有分工又相互协作关系的基础上 ,实现了网络化办公 ,进一步明确了责任 ;原始记录模板化使各科室检验人员减少了书写量 ,提高了准确率。1　药检所检品主流程待检样品→业务室登记→打印检品卡→分送至相关检验科室→检验科室检验 ,填写检验卡 (包括原始记录及检验结果 )→返卡业务室→根据检验科室检验结果打印检验报告书。依据以上流程 ,我们提出分流工…     

配对等级分组资料的秩和检验法

配对等级分组资料的秩和检验法陈和利（江西中医学院南昌３３０００６）在临床科研资料中，部分资料属多项分类计数资料，各属性间按有序等级进行分类计数，如临床疗效可分为：治愈、显效、好转、无效四个等级；化验结果分为：－、＋、、、等五个有序等级，此类资料...     

Testing specific hypotheses by fitting underlying distributions to categorical data

The problem of estimating parameters and testing hypotheses pertaining to categorical data is well known in statistical analysis. Much of the literature on the subject specifies and fits linear models to multinomial data using methods such as weighted least squares. This article describes maximum-likelihood estimation and likelihood ratio tests for ordered categorical response variates with either discrete or continuous underlying probability distributions. Emphasis is on fitting and making inferences about parameters of mixture distributions, especially mixtures of normal distributions. Goodness-of-fit tests are given to check the adequacy of the fitted distributional models.     

The back-step method--method for obtaining unbiased population parameter estimates for ordered categorical data

A significant bias in parameters, estimated with the proportional odds model using the software NONMEM, has been reported. Typically, this bias occurs with ordered categorical data, when most of the observations are found at one extreme of the possible outcomes. The aim of this study was to assess, through simulations, the performance of the Back-Step Method (BSM), a novel approach for obtaining unbiased estimates when the standard approach provides biased estimates. BSM is an iterative method involving sequential simulation-estimation steps. BSM was compared with the standard approach in the analysis of a 4-category ordered variable using the Laplacian method in NONMEM. The bias in parameter estimates and the accuracy of model predictions were determined for the 2 methods on 3 conditions: (1) a nonskewed distribution of the response with low interindividual variability (IIV), (2) a skewed distribution with low IIV, and (3) a skewed distribution with high IIV. An increase in bias with increasing skewness and IIV was shown in parameters estimated using the standard approach in NONMEM. BSM performed without appreciable bias in the estimates under the 3 conditions, and the model predictions were in good agreement with the original data. Each BSM estimation represents a random sample of the population; hence, repeating the BSM estimation reduces the imprecision of the parameter estimates. The BSM is an accurate estimation method when the standard modeling approach in NONMEM gives biased estimates.     

The back-step method—Method for obtaining unbiased population parameter estimates for ordered categorical data

A significant bias in parameters, estimated with the proportional odds model using the software NONMEM, has been reported. Typically, this bias occurs with ordered categorical data, when most of the observations are found at one extreme of the possible outcomes. The aim of this study was to assess, through simulations, the performance of the Back-Step Method (BSM), a novel approach for obtaining unbiased estimates when the standard approach provides biased estimates. BSM is an iterative method involving sequential simulation-estimation steps. BSM was compared with the standard approach in the analysis of a 4-category ordered variable using the Laplacian method in NONMEM. The bias in parameter estimates and the accuracy of model predictions were determined for the 2 methods on 3 conditions: (1) a nonskewed distribution of the response with low interindividual variability (IIV), (2) a skewed distribution with low IIV, and (3) a skewed distribution with high IIV. An increase in bias with increasing skewness and IIV was shown in parameters estimated using the standard approach in NON-MEM. BSM performed without appreciable bias in the estimates under the 3 conditions, and the model predictions were in good agreement with the original data. Each BSM estimation represents a random sample of the population; hence, repeating the BSM estimation reduces the imprecision of the parameter estimates. The BSM is an accurate estimation method when the standard modeling approach in NONMEM gives biased estimates.     

Multiple comparisons with two controls for ordered categorical responses

In clinical studies, ordered categorical responses are common. To compare the efficacy of several treatments with a control for ordinal responses, the normal latent variable model has recently been proposed. This approach conceptualizes the responses as manifestations of an underlying continuous normal variable. In this article, we extend this idea to develop the multiple comparison method for use when there are two controls in the clinical trial. The proposed method is constructed such that the familywise type I error rate is controlled at a prespecified level. In addition, for a given level of test power, the procedure to evaluate the required sample size is provided. The proposed testing procedure is also illustrated by an example from a clinical study.     

Pharmacokinetic-pharmacodynamic models for categorical toxicity data

We propose a pharmacokinetic-pharmacodynamic (PK/PD) model (with possibly different choices for the PD link) for categorical toxicity data analysis. This is extension of the one-comportment model that applies to toxic endpoints categorised by grades (e.g., benign, mild, severe, and very severe). The model assumes that the area under the curve (AUC) of the internal quantity of the chemical substance is the critical dose-metric that drives the acute toxic phenomenon. That model handles time-varying concentrations and takes into account follow-up time, i.e., time at which effects are observed. Moreover the model bridges mechanistically based dose-response models and standard dose-response models, retaining the advantages of both. We use Markov chain-Monte Carlo (MCMC) simulations to fit the model to mortality data for mice exposed to chlorine, rats exposed to ammonia, and categorical data (different severity levels) from acute exposures of rats and humans to hydrogen sulfide.     

A unified approach for assessing agreement for continuous and categorical data

This paper proposes several Concordance Correlation Coefficient (CCC) indices to measure the agreement among k raters, with each rater having multiple (m) readings from each of the n subjects for continuous and categorical data. In addition, for normal data, this paper also proposes the coverage probability (CP) and total deviation index (TDI). Those indices are used to measure intra, inter and total agreement among all raters. Intra-rater indices are used to measure the agreement among the multiple readings from the same rater. Inter-rater indices are used to measure the agreement among different raters based on the average of multiple readings. Total-rater indices are used to measure the agreement among different raters based on individual readings. In addition to the agreement, the paper also assess intra, inter, and total precision and accuracy. Through a two-way mixed model, all CCC, precision and accuracy, TDI, and CP indices are expressed as functions of variance components, and GEE method is used to obtain the estimates and perform inferences for all the functions of variance components. Each of previous proposed approaches for assessing agreement becomes one of the special case of the proposed approach. For continuous data, when m approaches infinity, the proposed estimates reduce to the agreement indices proposed by Barnhart et al. (2005). When m = 1, the proposed estimate reduces to the ICC proposed by Carrasco and Jover (2003). When m = 1, the proposed estimate also reduces to the OCCC proposed by Lin (1989), King and Chinchilli (2001a) and Barnhart et al. (2002). When m = 1 and k = 2, the proposed estimate reduces to the original CCC proposed by Lin (1989). For categorical data, when k = 2 and m = 1, the proposed estimate and its associated inference reduce to the kappa for binary data and weighted kappa with squared weight for ordinal data.     

Comparison of proportional and differential odds models for mixed-effects analysis of categorical data

In this work a model for analyzing categorical data is presented; the differential odds model. Unlike the commonly used proportional odds model, this model does not assume that a covariate affects all categories equally on the log odds scale. The differential odds model was compared to the proportional odds model, by assessing statistical significance and improvement of predictive performance when applying the differential odds model to data previously analyzed using the proportional odds model. Three clinical studies; 3-category T-cell receptor density data, 5-category diarrhea data and 6-category sedation data, were re-analyzed with the differential odds model. As expected, no improvements were seen with T-cell receptor density and diarrhea data. However, for the more complex measurement sedation, the differential odds model provided both statistical improvements and improvements in simulation properties. The estimated actual critical value was for all data lower than the nominal value, using the number of added parameters as the degree of freedom, i.e. the differential odds model is statistically indicated to a less extent than expected. The differential odds model had the desired property of not being indicated when not necessary, but it may provide improvements when the data does not represent a categorization of continuous data.     

Implementation and Evaluation of the SAEM Algorithm for Longitudinal Ordered Categorical Data with an Illustration in Pharmacokinetics–Pharmacodynamics

Analysis of longitudinal ordered categorical efficacy or safety data in clinical trials using mixed models is increasingly performed. However, algorithms available for maximum likelihood estimation using an approximation of the likelihood integral, including LAPLACE approach, may give rise to biased parameter estimates. The SAEM algorithm is an efficient and powerful tool in the analysis of continuous/count mixed models. The aim of this study was to implement and investigate the performance of the SAEM algorithm for longitudinal categorical data. The SAEM algorithm is extended for parameter estimation in ordered categorical mixed models together with an estimation of the Fisher information matrix and the likelihood. We used Monte Carlo simulations using previously published scenarios evaluated with NONMEM. Accuracy and precision in parameter estimation and standard error estimates were assessed in terms of relative bias and root mean square error. This algorithm was illustrated on the simultaneous analysis of pharmacokinetic and discretized efficacy data obtained after a single dose of warfarin in healthy volunteers. The new SAEM algorithm is implemented in MONOLIX 3.1 for discrete mixed models. The analyses show that for parameter estimation, the relative bias is low for both fixed effects and variance components in all models studied. Estimated and empirical standard errors are similar. The warfarin example illustrates how simple and rapid it is to analyze simultaneously continuous and discrete data with MONOLIX 3.1. The SAEM algorithm is extended for analysis of longitudinal categorical data. It provides accurate estimates parameters and standard errors. The estimation is fast and stable.