重复测量设计与随机区组设计原理及应用

重复测量设计与随机区组设计是医学研究领域两种重要的研究设计类型,二者在形式上常较为接近,尤其是当重复测量或区组因素均与时间效应有关时,更易使人产生混淆[1].在实际工作中,两者误用的情形并不少见.如此不仅会损失原始数据的部分信息(如重复测量数据内部的相关性),甚至有可能导致统计分析结论的错误.因此本文就重复测量设计与随机区组设计的自身特点及其区别作一番介绍,并运用实例对两种设计类型的SPSS软件分析结果进行分析和比较.     

随机区组设计方差分析中应注意的几个问题

目的探讨区组因素的概念及随机区组设计与分析的若干前提条件。方法对实例1进行配对、随机区组和单因素设计方差分析3种不同的处理,比较结果的异同。对实例2采取不考虑与考虑区组-处理交互作用两种不同的处理,比较结果的异同。结果实例1中3种分析方法前两种结果相同但与第3种方法的P值有所差异;实例2中,如果直接用随机区组设计的方差分析,会忽略可能存在的交互作用,使结果发生偏倚。结论随机区组设计属于单因素设计,配对设计是其特例。不主张抛开研究设计将区组方差分析变为单因素方差分析。利用残差散点图可对区组设计方差分析的前提条件进行考察,其中区组-处理之间是否存在交互作用可采用Turkey的单自由度检验进行判断。     

具有重复测量的多因素设计类型及方差分析

本文以两试验因素的三种不同安排为例,介绍了具有重复测量的多因素设计类型及其资料的方差分析方法。笔者认为:在析因设计中有意识地加入区组因素,可以提高对试验误差的估计精度。     

重复测量资料分析常见统计错误与软件实现

目的：近期医学期刊中常出现具有重复测量设计方面的论文，此论文剖析了文中常出现的错误。方法：本文围绕一个实例饭菜 示了不恰当做法，从理论上阐述了重复测量资料的正确分析方法。运用SAS软件及SPSS软件给出了两种资料处理方法结果：并对其进行了解释，结果：提示研究者避免出现此类统计错误，结论：由于重复测量数据间存在一定的自相关性和随机误差的多层次性，因而其分析方法有别于一般的统计分析方法。     

重复测量设计样本含量估计

目的 给出计算两个和多个处理组重复测量设计所需样本含量的公式。方法 探讨两组及多组重复测量设计的样本含量设计。结果 利用单因素裂区分析模型建立了处理组数G≥2时样本含量的估计公式。结论 列出了影响样本含量的因素,并指出Bloch‘s样本含量公式的低估现象。     

基于随机系数模型的重复测量资料分析方法

目的探讨随机系数模型在重复测量资料分析中的应用。方法以实例说明随机系数模型，包括线性随机系数模型和多项式随机系数模型在重复测量资料中的应用，并与重复测量方差分析进行比较。结果重复测量方差分析结果显示，处理因素无统计学意义，时间因素有统计学意义；随机系数模型拟合结果还提示因变量与时间之间可能存在非线性关系。结论随机系数模型拟合结果基本与重复测量方差分析结论一致，但随机系数模型可以考虑资料的相关性、非线性问题，因此更为准确可信。     

SPSS软件进行随机区组设计非参数检验的多重比较

[目的]随机区组设计多组样本资料非参数秩和检验后,在SPSS软件下实现各组样本的多重比较.[方法]采用秩转换技术结合随机区组设计的方差分析进行多重比较.[结果]对同一数据资料进行处理,所得结论相同.[结论]SPSS软件可实现非参数检验的多重比较.     

随机区组设计资料的Quade检验

原理与方法该检验由Ｑｕａｄｅ在 1972年提出 ,用于处理完全随机区组设计的资料。数据由ｂ个相互独立的ｋ维随机变量构成。数据排列的模式如表 1所示。表 1　随机区组设计的数据排列模式区　组 处　理　组12…ｋ1Ｘ1 1 Ｘ1 2 …Ｘ1ｋ2 Ｘ2 1 Ｘ2 2 … Ｘ2ｋ3Ｘ3 1 Ｘ3 2 … Ｘ3ｋ……………ｂＸｂ1 Ｘｂ2 …Ｘｂｋ　　应用条件要求各区组之间的数据相互独立 ,适用于数据类型为非正态分布的计量资料。检验假设Ｈ0为各处理组总体分布相同 ,Ｈ1为各处理组总体分布不全相同。检验统计量为Ｔ值 ,各种有关的计算公式如下。区组内极差 =区组内最大…     

多因素重复测量设计裂区方差分析

医学研究领域中 ,常见到重复测量 (ＲｅｐｅａｔｅｄＭｅａｓｕｒｅｍｅｎｔｓ,ＲＭ )数据。重复测量因素各水平间往往存在自相关性 ,而常规统计分析方法需满足数据间的独立性。重复测量设计中的研究因素分为重复测量因素 (ｗｉｔｈｉｎ -ｓｕｂｊｅｃｔｆａｃｔｏｒ ,即“组内因素”)和处理因素 (ｂｅｔｗｅｅｎ -ｓｕｂｊｅｃｔｆａｃｔｏｒ ,即“组间因素”) ,按所含重复测量因素的个数分为单因素重复测量 (Ｏｎｅ -ｗａｙＲｅｐｅａｔｅｄＭｅａｓｕｒｅｍｅｎｔｓ)设计和多因素重复测量(Ｍｕｌｔｉ-ｗａｙＲｅｐｅａｔｅｄＭｅａｓｕｒ…     

具有一个重复测量资料的协方差分析

重复测量资料在医学研究领域中广泛出现 ,设计类型和分析目的往往具有变异性〔1〕,困扰的统计分析方法可谓是复杂多样 ,均数的多重比较亦是扑朔迷离〔2～ 6〕。本文利用Ｓｔａｔａ软件的协方差分析程序尝试性地分析具有一个重复测量资料的一个实例〔7〕。分析方法1 设计类型假设有Ｎ个观察单位 ,随机分为Ｇ个处理组 ,第ｉ组 (ｉ=1,2 ,… ,Ｇ)的观察单位数为ｎｉ,第ｉ组的第ｊ个观察单位 (ｊ =1,2 ,… ,ｎｉ)在Ｔ种条件下重复测量到处理前ｋ(ｐｒｅ)个值和处理后ｋ(ｐｏｓｔ)个值〔ｋ(ｐｒｅ) +ｋ(ｐｏｓｔ) =ｋ ,ｋ =1,2 ,… ,Ｔ〕 ,第ｉ组…     

Misuse of ANOVA with cumulative intakes

Cumulative responses are important for the study of ingestive behavior. The total amounts consumed at several time points after experimental or control treatments are often analyzed using a mixed-model analysis of variance. The method used by some investigators to calculate the significance of this difference is invalid and may lead to serious errors of inference. Direct analysis of cumulative data can lead to grossly erroneous p-values for certain effects because data from early time points contribute disproportionately to the outcome. Analysis of uncumulated data yields the appropriate levels of significance and all of the same hypotheses can be tested.     

Conducting power analyses for ANOVA and ANCOVA in between-subjects designs

Researchers are frequently asked to justify the sample size used in their quantitative inquiries. Such a justification can be provided through a power analysis. Conducting power analyses, however, can raise some difficult issues regarding the specification of the size of the effect, testing for interaction effects, the role of covariates, and the use of an estimated effect size in the power analysis. The authors present methods for conducting power analyses along with a discussion of these issues, and they make available SAS programs that can be used to implement the power analyses that are discussed.     

不等距重复测量设计方差分析用SAS和SPSS实现的对比

目的探讨不等距重复测量设计方差分析的条件及结果解释。方法用SAS和SPSS软件实现不等距重复测量设计方差分析。结果给出了方差分析条件、SAS和SPSS程序及主要结果解释。结论只有满足方差分析条件、正确合理解释结果,才能保证不等距重复测量设计方差分析的有效性。     

A note on ANOVA assumptions and robust analysis for a cross-over study

Analysis of variance (ANOVA) methods are usually applied to analyse continuous data from cross-over studies. The analysis, however, may not have appropriate type I error when certain assumptions are violated. In this paper, we first clarify a conventionally minimum set of assumptions that validate the F-tests of ANOVA models for cross-over studies. We then provide a practical verification/remedy procedure based upon the theoretical developments. By applying the verification/remedy procedure, more robust analysis results can be expected from the ANOVA models.     

A Practical ANOVA Approach for Uncertainty Analysis in Population-Based Disease Microsimulation Models

Objectives

To provide a practical approach for calculating uncertainty intervals and variance components associated with initial-condition and dynamic-equation parameters in computationally expensive population-based disease microsimulation models.

哈尔滨市学龄儿童行为问题的多变量分析

目的研究行为问题儿童与正常儿童差异,为预防和干预提供线索。方法采用整群抽样的方法,选取4所小学,共196 1名学龄儿童,采用Achenbach儿童行为问卷(CBCL)评价儿童的行为。通过对儿童行为问题调研结果及对相应儿童的智力水平测验、精神运动功能测验、事件相关电位测验的数据,运用SAS软件进行了多因素方差分析方法,综合考虑各项指标,找出正常儿童与行为问题儿童的差异。结果交往不良异常儿童相对正常儿童智力水平整体偏低;强迫因子异常儿童相对正常儿童潜伏期偏短;社交因子异常儿童潜伏期偏长、波幅偏高;攻击因子异常儿童潜伏期偏短。结论应加强有行为问题儿童的纠正和教育。     

Application of functional ANOVA models for hazard regression to the Hisayama data

A methodology for modeling covariate effects on the time-to-event data is developed. The covariates are allowed to be time dependent and their effects are modeled using polynomial splines in order to account for possibly non-linear effects. The methodology is applied to examine the effects on the incidence brain infarction based on a cohort study in Hisayama, Japan. The results indicate that at least two non-linear effects are significant (body mass index and systolic blood pressure) and there is a time-varying drug effect. The resulting significant risk factors are assessed by the proposed method that is more flexible and hence less biased than the traditional procedures where linear effects are imposed. These results are extremely important to the local medical investigation. In particular, more insight has been gained by examining the non-linear effects.     

固定效应方差分析中四种离差平方和的定义与探讨

目的系统介绍并探讨固定效应方差分析中四种离差平方和计算方法,为正确进行方差分析提供参考。方法介绍四种离差平方和的原理,结合实例展示具体计算过程和它们相互间的联系,并用SPSS和SAS软件验证。结果对相关理论和具体计算方面都给出了较详尽的过程。结论方差分析应根据数据特征选择离差平方和的计算方法。     

A marginal‐mean ANOVA approach for analyzing multireader multicase radiological imaging data

The correlated‐error ANOVA method proposed by Obuchowski and Rockette (OR) has been a useful procedure for analyzing reader‐performance outcomes, such as the area under the receiver‐operating‐characteristic curve, resulting from multireader multicase radiological imaging data. This approach, however, has only been formally derived for the test‐by‐reader‐by‐case factorial study design. In this paper, I show that the OR model can be viewed as a marginal‐mean ANOVA model. Viewing the OR model within this marginal‐mean ANOVA framework is the basis for the marginal‐mean ANOVA approach, the topic of this paper. This approach (1) provides an intuitive motivation for the OR model, including its covariance‐parameter constraints; (2) provides easy derivations of OR test statistics and parameter estimates, as well as their distributions and confidence intervals; and (3) allows for easy generalization of the OR procedure to other study designs. In particular, I show how one can easily derive OR‐type analysis formulas for any balanced study design by following an algorithm that only requires an understanding of conventional ANOVA methods. Copyright © 2013 John Wiley & Sons, Ltd.     

Functional ANOVA with random functional effects: an application to event-related potentials modelling for electroencephalograms analysis

The differential effects of basic visual or auditory stimuli on electroencephalograms (EEG), named event related potentials (ERPs), are often used to evaluate the impact of treatments on brain performances. In the present paper, we propose a P-splines based model that can be used to evaluate treatment effect on the timing and the amplitude of some peaks of the ERPs curves. Functional ANOVA is an adaptation of linear model or analysis of variance to analyse functional observations. The changes in the functional of interest effects are generally described using smoothing splines. Eilers and Marx proposed to work with P-splines, a combination of B-splines and difference penalties on coefficients. We define a P-splines model for ERPs curves combined with random effects. In particular, we show that it is a useful alternative to classical strategies requiring the visual and usually imprecise localization of specific ERP peaks from curves with a low signal-to-noise ratio.