容易混淆的试验设计类型的辨析

含区组因素的析因设计、裂区设计和具有重复测量的设计被广泛应用于医学科研之中。它们在设计形式，方差分析模型等方面都非常相似，因而，在识别设计类型时易造成混淆。清楚地辨析各种容易混淆的试验设计类型，是对资料正确进行方差分析的首要前提。     

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。     

拉丁方实验的方差分析简化法

拉丁方是目前常用的一种实验设计方法,有一次性和重复性两种类型,其实验结果的统计学分析均采用方差分析法。鉴于拉丁方实验具有各因素(行间、列间、处理间、重复批间)划区内的例数相等的特点,可利用电子计算器将其方差分析方法简化。简化法的基本公式还适用于那些按因素划区时各区内例数相等的完全随机、区组随机、正交设计等实验资料的方差分析。一、基本公式设拉验丁方实验为k×k级,重复次数为m,则可     

北京市石景山区餐饮业量化分级评定结果分析

[目的]了解北京市石景山区餐饮业量化分级的级别分布情况以及量化分级工作中存在的问题,对餐饮业量化分级评定结果进行分析。[方法]从北京市卫生监督执法平台上下载石景山区餐饮业量化分级评定数据,使用SPSS11.0统计软件对数据进行分析。[结果]石景山区评为A级、B级和C级的餐饮单位数占了总数的96.4%,但是C级单位数占总数的60%以上;从小型-中型-大型餐馆饭店或单位职工食堂,A级和B级单位和所占的比重依次增大;中型学校食堂中,A级和B级单位和所占的比重最大;从托幼机构-中小学-大学食堂,A级和B级单位和所占的比重依次减少。[结论]石景山区餐饮单位整体食品卫生状况是合格,但是C级单位数所占比例太大;经营面积对餐馆饭店和单位职工食堂的级别构成影响较明显;学校性质对学校食堂的量化级别分布影响较明显。     

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。     

医务放射工作人员心血管健康异常的影响因素研究

目的 应用Logistic模型分析性别、年龄、工龄、工种以及单位级别等因素对医疗放射工作人员心血管健康状况的影响。了解长期低剂量辐射与放射工作人员心血管健康异常之间关系。方法 收集2013年山东省某市医务放射工作人员共437人的职业健康体检资料中关于心血管检查项目,录入EXCEL表格中,运用SPSS台22.0软件进行logistic回归统计分析。结果 对心血管系统健康影响因素分析结果如下:单因素分析的结果提示影响血压和心率异常的因素有性别、年龄和工龄(P < 0.001),统计回归分析揭示其中的性别、年龄是血压和心率异常的独立影响因素(P < 0.001);经单因素分析,在α=0.05水平上,性别、年龄、工龄、单位级别和工种皆不是心电图异常表现的影响因素。结论 性别和年龄是导致医务放射工作人员心血管健康异常的独立影响因素,而工龄、工种以及单位级别不是其影响因素。本结果提示长期低剂量辐射与放射工作人员心血管健康异常无显著关联性。     

鞋厂工人苯暴露的纵向资料分析

[目的]了解低浓度苯暴露对鞋厂工人外周血象的影响情况. [方法]对163名低浓度苯暴露工人进行连续3年的外周血象结果纵向观察,采用随机区组设计两因素方差分析法对结果进行分析. [结果]低浓度苯暴露2年WBC计数显著下降,平均下降0.41单位;苯暴露1年RBC计数下降0.18单位,暴露2年RBC计数基本恢复正常水平. [结论]低浓度苯暴露对鞋厂工人WBC和RBC计数仍有显著影响,应引起高度重视.     

REML法和Bayesian法对小样本不平衡单因素随机效应模型方差成分估计的模拟比较分析

目的 比较限制性极大似然估计(REML)法和贝叶斯法(Bayesian)对小样本不平衡单因素随机效应模型方差成分估计的偏差和精密度,同时考虑在样本量的大小、单位的数量和单位内相关系数(ICC)的大小不同的情况下对方差成分估计的精确程度的影响.方法 通过计算机模拟7组不同设计的数据集,用SAS软件MIXED模块进行方差成分估计.结果 不同的设计中,REML法估计比Bayesian法估计更加接近真值,但Bayesian法对组间方差的区间估计更加精密.对于两种方法 而言,样本和单位数量的增加,估计结果 更加准确.组内方差的估计,比组间方差的估计更准确和精密.结论 对小样本不平衡结构数据,当ICC为小或中等时,REML估计比Bayesian估计的偏差和均方误差要小,推荐使用.但是Bayesian法的区间估计比REML法的区间估计更加精密.     

一种基于脑电图的大脑可塑性研究

本文设计了一套手指按键运动测试系统,对19位健康被试者分别采集了安静状态下和测试-训练-测试-训练-测试5个模块下的实验数据。手指按键运动实验中,运动测试模块含有可对学习训练效果进行评价的参数,本文使用统计学的方差分析法对实验获取数据进行了研究分析,结果表明训练对运动测试结果有显著影响;使用相关性分析对大脑运动皮层的连通性进行研究,发现通过训练,在大脑运动区中超过80%的连结都得到了增强,尤其是C4电极所在位置的脑区,C4信号与其他所有电极信号的相关性都得到了增强。信号之间相关的增强与脑区之间联系的增强以及运动能力的提高有很明显的相关性。通过脑电图实验的研究分析结果,本文认为在运动训练的作用下,大脑的相关脑区之间的连通性提高了运动成绩,即大脑发生了可塑性变化。     

临床试验研究(四)

第五讲临床试验几种常用的设计方法在临床试验中,还是广泛应用单因素的设计方法。多因素设计则多见于临床实验室的研究。下面着重介绍临床试验中常用的几种设计方法。一、自身对照设计自身对照试验是在同一受试者身上进行。此种试验的设计有三种形式:(1)对每个观察单位进行两次观察,第一次观察不给处理,第二次观察给予处理,两次结果的差值作为实验效应;(2)第一次给予处理 A,第二次给予处理 B,也以两次结果的差值作为     

计算机电子线路原理图设计和印刷电路板图制作的介绍

本文介绍了计算机电子线路原理图设计和印刷电路板图的制作。     

医用线性回归变量变换及诊断

本文运用近代回归分析的原理和方法，将残差图和偏回归杠杆图作为诊断回归模型缺陷的工具，利用变量变换使资料满足线性回归模型的基本要求。实例分析表明，这些方法在诊断模型缺陷。决定相应处理措施以及改进模型回归效果等方面均取得了满意效果。     

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。     

Identifying inconsistency in network meta-analysis: Is the net heat plot a reliable method?

One of the biggest challenges for network meta-analysis is inconsistency, which occurs when the direct and indirect evidence conflict. Inconsistency causes problems for the estimation and interpretation of treatment effects and treatment contrasts. Krahn and colleagues proposed the net heat approach as a graphical tool for identifying and locating inconsistency within a network of randomized controlled trials. For networks with a treatment loop, the net heat plot displays statistics calculated by temporarily removing each design one at a time, in turn, and assessing the contribution of each remaining design to the inconsistency. The net heat plot takes the form of a matrix which is displayed graphically with coloring indicating the degree of inconsistency in the network. Applied to a network of individual participant data assessing overall survival in 7531 patients with lung cancer, we were surprised to find no evidence of important inconsistency from the net heat approach; this contradicted other approaches for assessing inconsistency such as the Bucher approach, Cochran's Q statistic, node-splitting, and the inconsistency parameter approach, which all suggested evidence of inconsistency within the network at the 5% level. Further theoretical work shows that the calculations underlying the net heat plot constitute an arbitrary weighting of the direct and indirect evidence which may be misleading. We illustrate this further using a simulation study and a network meta-analysis of 10 treatments for diabetes. We conclude that the net heat plot does not reliably signal inconsistency or identify designs that cause inconsistency.     

Visualizing covariates in proportional hazards model

We present a graphical method called the rank‐hazard plot that visualizes the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0, 1]. The relative hazard is plotted with respect to the reference hazard, which can be, for example, the hazard related to the median of the covariate. Transformation to scaled ranks allows plotting of covariates measured in different units in the same graph, which helps in the interpretation of the epidemiological relevance of the covariates. Rank‐hazard plots show the difference of hazards between the extremes of the covariate values present in the data and can be used as a tool to check if the proportional hazards assumption leads to reasonable estimates for individuals with extreme covariate values. Alternative covariate definitions or different transformations applied to covariates can be also compared using rank‐hazard plots. We apply rank‐hazard plots to the data from the FINRISK study where population‐based cohorts have been followed up for events of cardiovascular diseases and compare the relative importance of the covariates cholesterol, smoking, blood pressure and body mass index. The data from the Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT) are used to visualize nonlinear covariate effects. The proposed graphics work in other regression models with different interpretations of the y‐axis. Copyright © 2009 John Wiley & Sons, Ltd.     

同水平析因设计矩阵的分解结果及其特点

目的介绍同水平析因设计矩阵的分解方法、分解结果及其特点,为多因素试验设计提供一种有效方法.方法以n水平(n≥3)的同水平多因素析因设计矩阵为基本的设计矩阵,不增加任何限定条件,仅考察各试验点对其余试验点的影响,采用"主成分分析、聚类分析和规则归纳"等数据挖掘技术相结合的方法,可将此矩阵分解成一系列彼此互不重叠的独立设计矩阵.结果独立设计矩阵具有一些很好的特点,有些本身就是正交设计,有些本身就是均匀设计,还有些是它们所不包含的特殊设计,这些设计一般都是比较理想的多因素试验设计.结论独立设计不仅涵盖了多因素析因设计、分式析因设计、正交设计、均匀设计,还有一些是具有实用性的特殊设计,它是一类具有很高理论研究价值和实际应用前景的综合性极强的多因素试验设计方法.     

A note on graphical presentation of estimated odds ratios from several clinical trials

To display a number of estimates of a parameter obtained from different studies it is common practice to plot a sequence of confidence intervals. This can be useful but is often unsatisfactory. An alternative display is suggested which represents intervals as points on a bivariate graph, and which has advantages. When the data are estimates of odds ratios from studies with a binary response, it is argued that for either type of plot, a log scale should be used rather than a linear scale.     

Interpretation of interaction in factorial analysis of variance design

The validity of statistical conclusions in medical research depends on proper analysis and interpretation of collected data. One potential area of invalidity is the inappropriate post hoc analysis of statistically significant interactions in the analysis of variance of factorial designs. This paper examines the statistical explanations included in 83 studies published in three leading medical journals where the findings indicated significant interaction effects. Only 24 per cent of the reported statistically significant interactions had an accompanying correct interpretation. The most common form of misinterpretation involved a comparison of individual cell means within a row or column of one factor used in the design. This interpretation did not conform to the factorial ANOVA model with interaction. This misinterpretation occurs when the correct omnibus test of a hypothesis is followed by an incorrect post hoc analysis and/or an inaccurate assessment of the original statistical result.     

Theory of general balance applied to step wedge designs

A standard idealized step-wedge design satisfies the requirements, in terms of the structure of the observation units, to be considered a balanced design and can be labeled as a criss-cross design (time crossed with cluster) with replication. As such, Nelder's theory of general balance can be used to decompose the analysis of variance into independent strata (grand mean, cluster, time, cluster:time, residuals). If time is considered as a fixed effect, then the treatment effect of interest is estimated solely within the cluster and time:cluster strata; the time effects are estimated solely within the time stratum. This separation leads directly to scalar, rather than matrix, algebraic manipulations to provide closed-form expressions for standard errors of the treatment effect estimate. We use the tools provided by the theory of general balance to obtain an expression for the standard error of the estimated treatment effect in a general case where the assumed covariance structure includes random-effects at the time and time:cluster levels. This provides insights that are helpful for experimental design regarding the assumed correlation within clusters over time, sample size in terms of numbers of clusters and replication within cluster, and components of the standard error for estimated treatment effect.