生态学整合分析中两种常用效应值的实例应用比较

整合分析(meta-analysis)是对同一主题下多个独立实验结果进行综合的统计学方法,被认为是到目前为止最好的数量综合方法,其统计量为效应值,反应比(InR)和Hedges'd值是生态学应用中最常用的两个效应值。以综合植物生理生态学指标对大气CO₂浓度倍增响应为实例,比较这两种效应值的不同之处。采用两种效应值指标会对同一个生理生态指标产生不同的总效应值大小,有时甚至会改变效应值的方向;InR相对Hedges'd更易产生正效应值;Hedges'd较InR可拉大不同生理生态指标之间总效应值的差异;Hedges'd具有正负效应对称性,而InR却具有正负效应的不对称性。     

整合分析中结合效应值和总异质性的介绍

整合分析(meta-analysis)是对同一主题下多个独立实验结果进行综合的统计学方法,被认为是到目前为止最好的数量综合方法,其统计量为效应值。结合效应值(cumulative effect size)和总异质性(total heterogeneity)分别是整合分析中描述效应值中心趋向和变异程度的两个指标,是在整合分析中最重要的两个参数。在整合分析中随数据结构的不同又有多种求结合效应值和总异质性的方法。介绍了与三种数据结构(无结构数据、分类数据、连续数据)相对应的这两个指标的计算方法。     

基于Meta分析的土壤呼吸对降水改变的非对称响应

气候变暖已经引起全球降水格局改变。土壤呼吸作为陆地生态系统向大气释放CO₂最大的碳库,对降水变化的响应将进一步影响碳循环,从而对全球气候变化产生反馈。尽管以往已有大量关于土壤呼吸与降水变化关系的相关研究,但存在较大争议。因此,亟待进一步深入探究土壤呼吸对降水改变的响应。基于此,研究Meta分析方法,整合了来自Web of Science 英文数据库和中国知网文献数据库(CNKI)的284篇已发表的论文和367组数据,进而分析全球中低纬度地区土壤呼吸对降水改变的响应。研究结果表明,土壤呼吸对降水改变的响应呈现出非对称特征,降水量增加能够提高16.7%的土壤呼吸,而降水量减少则会抑制17.88%的土壤呼吸。研究还发现,不同生态系统和气候区域的土壤呼吸对降水改变的响应存在较大差别。其中,降水量增加能够提高草地生态系统22%的土壤呼吸,比森林生态系统土壤呼吸高出12%;而降水量减少则会削弱草地生态系统28%的土壤呼吸,这要比森林生态系统土壤呼吸还高16%。与湿润地区相比,降水量的增加对干旱地区土壤呼吸的促进作用更加明显。而降水量的减少对干旱地区和湿润地区土壤呼吸的影响均无显著差异。此外,本研究也证实了土壤呼吸对不同降水强度和年限的响应也存在差异。在不同降水强度上,无论增加降水还是减少降水,重度增减雨的土壤呼吸均改变最大,即:重度增减雨(＞75%)＞中度增减雨(25% -75%) ＞轻度增减雨(＜25%);在不同降水年限上,长期增雨对土壤呼吸的促进作用尤为突出,但长期减雨对土壤呼吸影响无显著差异。研究结果可为未来气候情景下陆地生态系统土壤呼吸变化的准确预测以及模型模拟和改进提供重要的科学依据和理论基础。     

Meta分析中几种常用效应值的介绍

效应值是定量Meta分析中的结合统计量,其计算方法主要依赖于对原文献数据的获取程度,介绍并比较适合3种原文献数据报道形式的几种效应值的计算方法。     

Meta分析中几种常用效应值的介绍

效应值是定量Meta分析中的结合统计量,其计算方法主要依赖于对原文献数据的获取程度,介绍并比较适合3种原文献数据报道形式的几种效应值的计算方法。     

Meta分析中几种常用效应值的介绍

效应值是定量Meta分析中的结合统计量,其计算方法主要依赖于对原文献数据的获取程度,介绍并比较适合3种原文献数据报道形式的几种效应值的计算方法。     

基于线性混合效应的红松人工林枝条生物量模型

基于黑龙江省孟家岗林场60株红松解析木3643个枝条生物量的实测数据,利用全部子回归技术建立了枝条生物量模型(枝、叶和枝总生物量模型),最终选择lnw=k₁+k₂lnL_b+k₃lnD_b为枝条生物量最优基础模型.利用SAS 9.3统计软件的PROC MIXED模块建立枝条生物量混合模型,并采用AIC、BIC、对数似然值和似然比等统计指标评价不同模型的拟合效果.结果表明: 红松解析木的叶和枝总生物量混合模型以k₁、k₂、k₃作为随机效应参数的拟合效果最好,而枝生物量混合模型以k₁、k₂作为随机效应参数的拟合效果最好.最后将枝条生物量最优基础模型与最优混合模型进行模型检验.混合模型各项指标优于基础模型,能有效地提高模型的预估精度,并且通过方差协方差结构校正随机参数来反映树木之间的差异.     

单和同类随机效应模型的局部影响判断

利用局部影响方法对线性模型中的单向分类随机效应模型进行了讨论。这种方法的优点在于避免了线性模型中有些参数估计不便于进行单点删除的诊断分析。     

小家鼠种群中长期预测—灰色系统模型及随机序列分析

小家鼠种群中长期波动受很多因素影响,采用灰色系统模型及随机序列分析的方法可以较好地从宏观上预测其波动趋势。本文以灰色系统GM(1,1)模型为基础,利用历年小家鼠种群数量的调查值,对其变动趋势进行预测;然后,对预测剩余误差进行随机序列分析,建立线性预测式预测随机项;趋势预测值与随机项预测值的和,做为小家鼠种群中长期波动的预测。对新疆天山北麓农区1969年至1979年小家鼠自然种群数量的模拟结果表明,本文中的方法是较为简便有效的。     

精神分裂症中氧化压力标记物的荟萃分析

<正>精神分裂症(Schizophrenia)是一种严重的神经精神系统疾病,目前对其病理过程中的神经化学机制还不清楚.已有研究提示,氧化压力与精神分裂症存在潜在联系,比如对精神分裂症病人脑组织的转录     

Developing jellyfish strategy hypotheses using circulation models

Four species of ergasilid copepods were collected from gill filaments of three species of fishes from Khor al-Zubair Lagoon, Iraq. The mugilid Liza subviridis hosted the new species Ergasilus iraquensis and Ergasilus pararostralis. Ergasilus synanceienis sp. n. was found on the synanceiid Leptosynanceia melanostigma(Day). The fourth species, Dermoergasilus varicoleus Ho, Jayarajan & Radhakrishnan, 1992 was found parasitizing the mugilid Liza abu, and is a new record for Iraq.     

On the equivalence of meta-analysis using literature and using individual patient data

When data come from several independent studies for the purpose of estimating treatment control differences, meta-analysis can be carried out either on the best linear unbiased estimators computed from each study or on the pooled individual patient data modelled as a two-way model without interaction, where the two factors represent the different studies and the different treatments. Assuming that observations within and between studies are independent having a common variance, Olkin and Sampson (1998) have obtained the surprising result that the two meta-analytic procedures are equivalent, i.e., they both produce the same estimator. In this article, the same equivalence is established for the two-way fixed-effects model without interaction with the only assumption that the observations across studies be independent. A consequence of the equivalence result is that, regardless of the covariance structure, it is possible to get an explicit representation for the best linear unbiased estimator of any vector of treatment contrasts in a two-way fixed-effects model without interaction as long as the studies are independent. Another interesting consequence is that, for the purpose of best linear unbiased estimation, an unbalanced two-way fixed-effects model without interaction can be treated as several independent unbalanced one-way models, regardless of the covariance structure, when the studies are independent.     

Assessing the implications of publication bias for two popular estimates of between-study variance in meta-analysis

Perhaps the greatest threat to the validity of a meta-analysis is the possibility of publication bias, where studies with interesting or statistically significant results are more likely to be published. This obviously impacts on inference concerning the treatment effect but also has implications for estimates of between-study variance. Two popular and established estimation methods are considered and formulae for assessing the implications of the bias are provided in terms of a general process for selecting studies. Meta-analysts, concerned that publication bias may be present, can use these as part of a sensitivity analysis to assess the robustness of their estimates of between-study variance using any selection process that is likely to be used in practice. The procedure is illustrated using a meta-analysis of clinical trials concerning the effectiveness of endoscopic sclerotherapy for preventing death in patients with cirrhosis and oesophagogastric varices.     

A unification of models for meta-analysis of diagnostic accuracy studies

Studies of diagnostic accuracy require more sophisticated methods for their meta-analysis than studies of therapeutic interventions. A number of different, and apparently divergent, methods for meta-analysis of diagnostic studies have been proposed, including two alternative approaches that are statistically rigorous and allow for between-study variability: the hierarchical summary receiver operating characteristic (ROC) model (Rutter and Gatsonis, 2001) and bivariate random-effects meta-analysis (van Houwelingen and others, 1993), (van Houwelingen and others, 2002), (Reitsma and others, 2005). We show that these two models are very closely related, and define the circumstances in which they are identical. We discuss the different forms of summary model output suggested by the two approaches, including summary ROC curves, summary points, confidence regions, and prediction regions.     

Bayesian nonparametric meta-analysis using Polya tree mixture models

Summary .   A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations. Stemming from asymptotic theory, study-specific summary statistics are modeled according to normal distributions with means representing latent true effect measures. A parametric approach subsequently models these latent measures using a normal distribution, which is strictly a convenient modeling assumption absent of theoretical justification. To eliminate the influence of overly restrictive parametric models on inferences, we consider a broader class of random effects distributions. We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random effects. An application involving meta-analysis of epidemiologic studies designed to characterize the association between alcohol consumption and breast cancer is presented, which together with results from simulated data highlight the performance of PTMs in the presence of nonnormality of effect measures in the source population.     

两种粪便保存方法对肠道菌群结构研究的影响

目的比较2种粪便保存方法（室温法和Invitek公司的粪便稳定剂保存法）对菌群结构研究的影响。方法应用PCR-变性梯度凝胶电泳技术（PCR-DGGE）方法,对用2种方法保存的3位志愿者粪便样品进行菌群结构的分析。结果室温法保存粪便样品24h后,S1个体菌群结构与原始样品的菌群结构相似度为83％,S2和S3的菌群结构与其原始样品的相似度仅为77％。而使用粪便稳定剂保存1d,期间各时间点样品菌群结构与原始样品相比变化较小,相似度在80％-90％。结论粪便稳定剂具有一定的稳定样品菌群结构的作用,在新鲜粪佰样品不能寺刻讲行深冻的情况下,使用粪便稳定剂是一种较好的样品保存方法。     

Bayesian meta-analysis for longitudinal data models using multivariate mixture priors

We propose a class of longitudinal data models with random effects that generalizes currently used models in two important ways. First, the random-effects model is a flexible mixture of multivariate normals, accommodating population heterogeneity, outliers, and nonlinearity in the regression on subject-specific covariates. Second, the model includes a hierarchical extension to allow for meta-analysis over related studies. The random-effects distributions are decomposed into one part that is common across all related studies (common measure), and one part that is specific to each study and that captures the variability intrinsic between patients within the same study. Both the common measure and the study-specific measures are parameterized as mixture-of-normals models. We carry out inference using reversible jump posterior simulation to allow a random number of terms in the mixtures. The sampler takes advantage of the small number of entertained models. The motivating application is the analysis of two studies carried out by the Cancer and Leukemia Group B (CALGB). In both studies, we record for each patient white blood cell counts (WBC) over time to characterize the toxic effects of treatment. The WBCs are modeled through a nonlinear hierarchical model that gathers the information from both studies.     

Differential gene expression detection and sample classification using penalized linear regression models

Differential gene expression detection and sample classification using microarray data have received much research interest recently. Owing to the large number of genes p and small number of samples n (p > n), microarray data analysis poses big challenges for statistical analysis. An obvious problem owing to the 'large p small n' is over-fitting. Just by chance, we are likely to find some non-differentially expressed genes that can classify the samples very well. The idea of shrinkage is to regularize the model parameters to reduce the effects of noise and produce reliable inferences. Shrinkage has been successfully applied in the microarray data analysis. The SAM statistics proposed by Tusher et al. and the 'nearest shrunken centroid' proposed by Tibshirani et al. are ad hoc shrinkage methods. Both methods are simple, intuitive and prove to be useful in empirical studies. Recently Wu proposed the penalized t/F-statistics with shrinkage by formally using the (1) penalized linear regression models for two-class microarray data, showing good performance. In this paper we systematically discussed the use of penalized regression models for analyzing microarray data. We generalize the two-class penalized t/F-statistics proposed by Wu to multi-class microarray data. We formally derive the ad hoc shrunken centroid used by Tibshirani et al. using the (1) penalized regression models. And we show that the penalized linear regression models provide a rigorous and unified statistical framework for sample classification and differential gene expression detection.