昆虫种群数据分析及在SPSS软件上的实现

选择合适的统计分析方法对昆虫种群分析至关重要。本文以昆虫种群数据常用的分析方法为基础,介绍了单因素方差分析、多因素方差分析、重复测量方差分析和回归分析等多种分析方法的基本原理,强调了各种分析方法的应用前提,避免误用方法导致结果判读产生偏差,并结合SPSS软件的使用,实现相应的分析,旨在为昆虫种群数据分析提供方法论的参考。     

中药广地龙中纤维蛋白溶酶提取工艺的优化

通过正交设计实验和利用国际上流行的统计分析软件SPSS10.0对蚓纤溶酶的提取结果进行方差分析,考察了提取时间、提取次数和溶剂体积3个因素对提取效果的影响,并考察了硫酸铵盐析的最适浓度段,得到了一条较好的提取工艺。     

Excel在药学数据分析工作中的应用

目的：采用常用的电子表格处理系统Microsoft Excel解决药学实验过程中遇到的数据分析问题。方法：应用工作表函数中内置的统计函数,以线性回归为例说明源数据的输入与结果返回的具体操作过程;对数据分析工具中的＂描述统计＂工具、t检验与方差分析,结合具体实例对药学实践中遇到的药学统计实际问题进行综合探讨。结果：用Excel表中内置的统计函数工具进行线性回归分析,方法简单、结果可靠;Excel表中的数据分析工具适用于日常药学实验数据分析过程中遇到的描述统计分析、t检验与方差分析。Excel与其它数据处理软件相比具有操作快捷、使用方便、计算精确、易于学习与掌握等优点。结论：Excel友好的界面,清晰的统计分析结果,使医药工作者在使用Excel的数据分析软件时会感到非常的方便快捷,灵活实用,值得在药学实践中应用推广。     

光照对深海热液喷口周围可培养微生物的生长影响

研究采用析因实验设计,探讨了光照和培养温度对深海热液喷口周围可培养微生物生长繁殖的影响。样品培养10天后,对培养液中细菌量进行显微计数,实验数据用SPSS11.0统计软件中的方差分析程序进行处理。统计结果显示:在标准大气压下,在33℃、50℃和65℃三个实验温度中,当温度为33℃时,光照培养液中细菌浓度约为暗培养液中的3.5倍;而在50℃和65℃的培养液中其细菌浓度均比暗培养液中的要低。研究结果为进一步获取、认识与开发利用深海喷口周围细菌资源打下了基础。     

小麦品种联合试验效应及理论依据──Ⅱ.联合方差分析的一些理论考虑

本文以一个国家级的小麦品种试验结果为材料,检验了方差分析的基本假定相符情况．结果表明,在总体上14个试点间方差不同质．对14个试点分别进行3项检验,其中有7个试点的结果违背了假定．对违背假定的试点数据在统计分析前应先作数据转换后再作方差分析．     

《临床医学研究中的统计分析和图形表达实例详解》书讯

<正>一本临床医生看得懂、用得上的统计书《临床医学研究中的统计分析和图形表达实例详解》。由周登远、崔壮、焦振山主编,军事医学科学出版社出版,定价43元。本书分为预备篇、统计分析篇和统计绘图篇。预备篇介绍了统计学基本知识、统计方法的选择和如何采用EpiD ata或SPSS建立数据文件;统计分析篇以SPSS 18中文版为介绍对象,实例解说了计量资料、计数     

《临床医学研究中的统计分析和图形表达实例详解》书讯

正一本临床医生看得懂、用得上的统计书《临床医学研究中的统计分析和图形表达实例详解》。由周登远、崔壮、焦振山主编,军事医学科学出版社出版,定价43元。本书分为预备篇、统计分析篇和统计绘图篇。预备篇介绍了统计学基本知识、统计方法的选择和如何采用EpiData或SPSS建立数据文件;统计分析篇以SPSS 18中文版为介绍对象,实例解说了计量资料、计数资料、生存资料和诊断试验中的统计分析,涵盖了临     

多元方差分析中的单一自由度独立比较

本文根据一元方差分析的单一自由度比较原理提出了多元方差分析中的单一自由度比较方法,以用于各种平衡试验设计中具多个变量的处理间多重比较．     

生物实验数据的单因素方差分析

结合实例详细地叙述了生物学实验数据的单因素方差分析计算方法,介绍了根据计算结果如何分析组群间的差异显著性,具有较强的实用意义.     

Bt与EoNPV混用配比优劣性图谱分析

苏云金杆菌（Bt）和茶尺蠖核型多角体病毒（EoNPV）等,两类生物源杀虫剂常以复配混用方式应用.由于药效的迟缓,而明显表现出剂量-时间-致死作用复合特征.但目前尚缺乏与之相适应的混用配比优劣判别标准.受叶庆华等地学信息图谱分析和曹进等指纹图谱整体相似性分析理论和方法的启发,用室内人工饲养繁殖茶尺蠖2龄初—中期幼虫生物测定结果作为基础数据,分析研究了Bt与EoNPV混用的剂量-时间-致死作用复合特征,并进行了配比优劣性判别的尝试.首先将生物测定的有关结果输入SPSS统计分析软件包和Excel绘图软件,进行多因素方差分析,同时分别绘出不同药剂处理的,以剂量梯度为横轴,以累加死亡虫数为纵轴的不同观察时段害虫致死过程的曲线组图,简称时段药效信息图;再使用SPSS软件包进行单因素方差分析,提取出用于整体相似性比较的图谱单元Ⅰ和用于细节非相似性比较的图谱单元Ⅱ,且分别量化,由此得到相似值和非相似值,据此又分别算得相似系数和非相似系数;最后综合成一种混用配比优劣性总体判别指标,简称Q值.结果,Bt：EoNPV为9：1、7：3、4：1和2：3等4个混配处理的Q值依次为200、100、31.0和23.8,明显标示了其中“9：1”的Q值最大,被确认为最合理的混配处理.此结果与之前的研究相同.还对该图谱分析方法的可靠性以及应用的可行性等进行了讨论.     

Statistical Analysis of Multiplex Brain Gene Expression Images

Analysis of variance (ANOVA) was employed to investigate 9,000 gene expression patterns from brains of both normal mice and mice with a pharmacological model of Parkinson's disease (PD). The data set was obtained using voxelation, a method that allows high-throughput acquisition of 3D gene expression patterns through analysis of spatially registered voxels (cubes). This method produces multiple volumetric maps of gene expression analogous to the images reconstructed in biomedical imaging systems. The ANOVA model was compared to the results from singular value decomposition (SVD) by using the first 42 singular vectors of the data matrix, a number equal to the rank of the ANOVA model. The ANOVA was also compared to the results from non-parametric statistics. Lastly, images were obtained for a subset of genes that emerged from the ANOVA as significant. The results suggest that ANOVA will be a valuable framework for insights into the large number of gene expression patterns obtained from voxelation.     

Plug‐in tests for nonequivalence of means of independent normal populations

In this paper, we consider the problem of testing nonequivalence of several independent normal population means. It is a well‐known problem to test the equality of several means using the analysis of variance (ANOVA). Instead of determining the equality, one may consider more flexible homogeneity, which allows a predetermined level of difference. This problem is known as testing nonequivalence of populations. We propose the plug‐in statistics for two different measures of variability: the sum of the absolute deviations and the maximum of the absolute deviations. For each test, the least favorable configuration (LFC) to ensure the maximum rejection probability under the null hypothesis is investigated. Furthermore, we demonstrate the numerical studies based on both simulation and real data to evaluate the plug‐in tests and compare these with the range test.     

K‐Sample comparisons using propensity analysis

In this paper, we investigate K‐group comparisons on survival endpoints for observational studies. In clinical databases for observational studies, treatment for patients are chosen with probabilities varying depending on their baseline characteristics. This often results in noncomparable treatment groups because of imbalance in baseline characteristics of patients among treatment groups. In order to overcome this issue, we conduct propensity analysis and match the subjects with similar propensity scores across treatment groups or compare weighted group means (or weighted survival curves for censored outcome variables) using the inverse probability weighting (IPW). To this end, multinomial logistic regression has been a popular propensity analysis method to estimate the weights. We propose to use decision tree method as an alternative propensity analysis due to its simplicity and robustness. We also propose IPW rank statistics, called Dunnett‐type test and ANOVA‐type test, to compare 3 or more treatment groups on survival endpoints. Using simulations, we evaluate the finite sample performance of the weighted rank statistics combined with these propensity analysis methods. We demonstrate these methods with a real data example. The IPW method also allows us for unbiased estimation of population parameters of each treatment group. In this paper, we limit our discussions to survival outcomes, but all the methods can be easily modified for any type of outcomes, such as binary or continuous variables.     

Prevalidation statistical design to assess analytical methods example of a quick liquid chromatographic assay of itraconazole in serum

(A) An analysis-of-varience spreadsheet program is presented which allows to readily test and/or quantitate in a single run analytical linearity, matrix effect on recovery, repeatability of measurement and of extraction and the ruggedness of these features for up to three sessions. Owing to napierian logarithmic transformation, ANOVA mean squares directly read as relative standard deviations and checking linearity is straightforward. (B) A quick assay for therapeutic drug monitoring of itraconazole and its main metabolite was devised with the help of the program, and subsequently validated according to current quality control recommendations. The assay involves acetonitrile demixing extraction, reversed-phase HPLC and UV detection and shows acceptable performance from 0.06 to 5.0 mg/l (limit of detection about 0.03 mg/l). The prevalidation design fairly predicted precision and accuracy, was more informative about matrix effect and was even more demanding about analytical linearity.     

A marginal model approach for analysis of multi-reader multi-test receiver operating characteristic (ROC) data

The receiver operating characteristic curve is a popular tool to characterize the capabilities of diagnostic tests with continuous or ordinal responses. One common design for assessing the accuracy of diagnostic tests involves multiple readers and multiple tests, in which all readers read all test results from the same patients. This design is most commonly used in a radiology setting, where the results of diagnostic tests depend on a radiologist's subjective interpretation. The most widely used approach for analyzing data from such a study is the Dorfman-Berbaum-Metz (DBM) method (Dorfman et al., 1992) which utilizes a standard analysis of variance (ANOVA) model for the jackknife pseudovalues of the area under the ROC curves (AUCs). Although the DBM method has performed well in published simulation studies, there is no clear theoretical basis for this approach. In this paper, focusing on continuous outcomes, we investigate its theoretical basis. Our result indicates that the DBM method does not satisfy the regular assumptions for standard ANOVA models, and thus might lead to erroneous inference. We then propose a marginal model approach based on the AUCs which can adjust for covariates as well. Consistent and asymptotically normal estimators are derived for regression coefficients. We compare our approach with the DBM method via simulation and by an application to data from a breast cancer study. The simulation results show that both our method and the DBM method perform well when the accuracy of tests under the study is the same and that our method outperforms the DBM method for inference on individual AUCs when the accuracy of tests is not the same. The marginal model approach can be easily extended to ordinal outcomes.     

MINQUE and ANOVA Estimator for One-way Classification - a Risk Comparison

For the one-way classification in unbalanced case MINQUEstimator for components of variance are given in a more explicit form than it is done in the paper from C. R. RAO (1971). By means of the risk functions we compare MINQUE and ANOVA estimator. For given n_j-patterns angular ranges in the positive quadrant are given where MINQUE is better than ANOVA estimator. A special n_j-pattern and one parameter δ₀ is found for which MINQUE is uniformly better than ANOVA. Limit values are given for MINQUE for δ₀ = ∞ and δ₀ = 0 and their relations to the ANOVA estimator are considered. The coincidence between MINQUE and ANOVA for balanced case is verified. Extensive numerical studies for real data are carried out which stimulated the search for a fixpoint δ as a point for which the distance to the initial parameter δ₀ is as small as possible.     

Methods for analysing cardiovascular studies with repeated measures

Background. Repeated measurements in a single subject are generally more similar than unrepeated measurements in different subjects. Unrepeated analyses of repeated data cause underestimation of the treatment effects. Objective. To review methods adequate for the analysis of cardiovascular studies with repeated measures. Results. (1) For between-subjects comparisons, summary measures and random-effects mixedlinear models are possible. Examples of summary measures include the area under the curve of drug time-concentration and time-efficacy curves, maximal values, mean values, and changes from baseline. A problem is that precision is lost because averages, rather than individual data, are applied. Random-effects mixed-linear models, available in SPSS statistical software and other software programmes, provide better precision for that purpose. (2) For within-subjects comparisons, repeated-measures ANOVAs are available in SPSS and other software programmes. Subgroup factors such as gender differences and age class can be included. Discussion. For non-Gaussian data, Wilcoxon's and Friedman's tests are available, for binary data McNemar's tests can be used in case of two repeated observations. No standard methods are available for repeated binary measures with more than two observations. The purpose of this review was not to present a complete report but, rather, to underline that ample efforts should be made to account for the special nature of repeated measures. (Neth Heart J 2009;17:429–33.)     

Statistical tests for differential expression in cDNA microarray experiments

Extracting biological information from microarray data requires appropriate statistical methods. The simplest statistical method for detecting differential expression is the t test, which can be used to compare two conditions when there is replication of samples. With more than two conditions, analysis of variance (ANOVA) can be used, and the mixed ANOVA model is a general and powerful approach for microarray experiments with multiple factors and/or several sources of variation.     

The effects of differential gene expression on coding sequence features: analysis by one-way ANOVA

It is well-established that non-random patterns in coding DNA sequence (CDS) features can be partially explained by translational selection. Recent extensions of microarray and proteomic expression data have stimulated many genome-wide investigations of the relationships between gene expression and various CDS features. However, only modest correlations have been found. Here we introduced the one-way ANOVA, a more powerful extension of previous grouping methods, to re-examine these relationships at the whole genome scale for Saccharomyces cerevisiae, where genome-wide protein abundance has been recently quantified. Our results clarify that coding sequence features are inappropriate for use as genome-wide estimators for protein expression levels. This analysis also demonstrates that one-way ANOVA is a powerful and simple method to explore the influence of gene expression on CDS features.