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。     

整合分析中两种假设模型的介绍及实例分析*

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。在进行整合分析时,首选应提出统计假设,根据假设的不同可将整合分析分为固定效应模型(fixedeffect model)和随机效应模型(random effect model),前者假定有相似的多个研究在同一分组里有一个共同的真实效应值,由于取样误差,导致在实际效应值的测定中各研究间存在差别;在后者中,假定各研究间有随机变量,因此,不共享一个真实效应值。介绍了两种假设模型下整合分析的计算方法,并进行了实例分析。     

方差组分估计方法MIVQUE和REML的模拟比较

利用ＭｏｎｔｅＣａｒｌｏ方法,对４种数据结构进行了ＭＩＶＱＵＥ和ＲＥＭＬ两种方差组分估计方法的模拟比较。方差组分估计所用的模型为奶牛育种中常用的公畜模型,它包括场年季固定效应、公牛组固定效应和公牛随机效应。４种数据结构中最大的有１２８４７个观察值,场年季效应和公牛效应水平数分别为７７８和４７,它与北京市目前可利用的奶牛头胎产奶量记录资料相当。最小的数据结构只有２００个观察值,１４８个场年季和２０头公牛。比较指标为估计值的偏差和方差（理论的或根据１０００次重复模拟所得的经验值）。结果表明,对于较大样本的数据结构,两种方法差异很小,它们间的估计值的相关接近于１,偏差小于真值的１％,方差近似相等。对于较小样本的数据结构,ＭＩＶＱＵＥ则明显优于ＲＥＭＬ。本研究还表明,对于ＲＥＭＬ来说,类似数据结构１的样本已能满足其渐近无偏性和有效性的大样本特性。     

基于概率话题模型的微生物菌群结构研究

微生物菌群结构的异质性在影响宿主健康与疾病等方面有着十分重要的作用.对于菌群结构的时间与空间尺度异质性研究主要有非监督学习算法以及监督学习算法.由于菌群数据特性与文本数据特性之间的相似性,本文采用非监督学习的LDA概率话题模型对菌群结构的时间异质性进行研究,并与系统聚类和K-Means聚类这两种方法进行比较.采用LDA模型折叠Gibbs抽样的蒙特卡洛算法对两种数据源北平顶猴(Macaca leonina)阴道菌群(MVB)和轻微型肝性脑病(MHE)菌群的时间异质性OTUs数据集进行了分析.用LDA模型分别将MVB和MHE数据源中的27个样本和77个样本的OTUs数据集分为6个Topic和4个Topic.这与系统聚类和K-Means聚类划分成的簇数目(分别为5,3与4,3)有所不同.此外,实验表明结合MVB样本间生理数据-pH和MHE中样本α多样性,pH和α值的分类相似性更能与LDA模型的样本分类特性保持一致.因此,LDA在样本的聚集程度上更能精确地对OTUs数据集进行分类.更为重要的是,LDA模型还可以鉴定出每个Topic中具有代表性的OTUs.与系统聚类和K-Means聚类方法相比较,LDA模型不仅能更为有效地量化菌群结构的异质性,还能鉴定出相对应影响异质性的OTUs.     

生态学与医学中的整合分析(Meta-analysis)

整合分析是针对一系列独立研究结果进行定量综合分析的方法。自从 1976年 Glass在心理学研究中提出以来 ,该方法已经在许多学科特别是医学领域进行了广泛的应用。 2 0世纪 90年代 ,整合分析被引入生态学研究 ,引起了生态学家和统计学家的广泛关注。在我国 ,该方法也于 1998年被引入生态学。由于生态学研究自身的特点 ,整合分析在应用时出现了许多新问题 ,如不同研究类型的数据抽提与转换、效应值的构建、研究间相关性的估计、出版偏见的评估与修正等 ,为此以整合分析应用最活跃的医学领域进行对比和借鉴 ,分析该方法在两个研究领域应用的范围和特点 ,讨论影响其在生态学中应用的各种因素 ,并着重阐述和探讨其在生态学应用中存在的问题和发展前景     

荧光定量PCR检测结核分枝杆菌Meta分析

目的系统评价荧光定量PCR(FQ-PCR)方法检测结核分枝杆菌的效果。方法按照系统评价的要求检索CBM、VIP、CNKI以及万方数据库等,获得20篇符合纳入标准的文献,对其进行Meta分析,并评价Meta分析结果的稳定性和发表偏倚。结果 FQ-PCR对照涂片染色、培养鉴定以及总数据的异质性检验P0.00001,采用随机效应模型进行Meta分析,其余的采用固定效应模型分析。FQ-PCR与涂片染色、培养鉴定、抗体检测等的总体效应Z值分别为7.76、5.00和7.34,P值均小于0.00001,差异具有统计学意义。总数据分析结果的合并OR=2.78,95%CI为1.93-4.01,总体效应检验,Z=5.49,P0.00001,差异具有统计学意义,固定效应模型OR值和95%CI(2.52[2.35-2.70])与随机效应模型比较接近,剔除小样本报道后的合并OR=2.93,95%CI为1.98-4.31,与剔除前的结果也比较接近。结论从现有的临床证据来看,FQ-PCR是检测结核分枝杆菌的有效方法,可推广应用与临床结核病辅助检测。     

籼米淀粉粘滞性的基因型与环境互作研究

水稻精米中大约含有90%的淀粉,因此淀粉的特性对水稻的食味品质有很大的影响.淀粉粘滞性是预测稻米食用、蒸煮和加工品质的重要指标.本研究利用4个细胞质雄性不育系和8个恢复系配置的不完全双列杂交组合来分析淀粉粘滞性指标(崩解值、回复值和消减值)的胚乳、细胞质和母体基因效应及环境互作效应.结果表明在崩解值、回复值和消减值的遗传变异中,遗传主效应方差分量占了64%以上,表明它们主要受遗传主效应控制,同时也受到基因型与环境互作效应的影响.崩解值、回复值和消减值的总遗传率分别为67.8%、79.5%、79.5%,而且普通遗传率占了总遗传率的75%以上,表明对这些性状的早世代选择有效,且在不同环境中选择效果相对稳定.     

Clonal performance of Scirpus yagara in multiple levels of substrate heterogeneity and submergence

荆三棱在多等级基质异质性与水淹处理下的克隆表现 环境异质性可以影响克隆水生植物的表现。鲜有研究者关注两个层次的环境异质性并将其融入 对克隆植物生态学的研究中。本研究的目的是: (1)检验不同基质异质性与水淹处理是否对植物表现产生相 似效应，(2)探索克隆植物的觅食行为。本研究将荆三棱(Scirpus yagara)置于不同基质异质性与水淹处理之中。基质处理包括1个均质性基质处理(湖泥与沙等体积混合)与3个异质性基质处理(湖泥斑块与沙斑块交错构建的两斑块、四斑块与八斑块基质)。水淹处理包括：0、10和30 cm。本实验测量了克隆分株数、克隆代数、叶数、球茎数、克隆分株高度、茎长、根状茎长、克隆半径、间隔子长、间隔子厚度、总生物量、球茎生物量与单个球茎生物量等性状数据。研究结果表明，水位上升导致克隆分株数、克隆代数、叶数和球茎数显著减少，同时基质异质性造成间隔子长度与间隔子厚度的显著变化。水位与基质异质性两因子对克隆分株数、叶数和间隔子长度产生了显著的交互效应。在两斑块基质与四斑块基质中，荆三棱对湖泥斑块表现出显著的觅食行为，更多的构件被放置于湖泥斑块中。尤其在两斑块基质中，所有的构件被放置于湖泥斑块中。在八斑块基质中，荆三棱表现出双向觅食，这导致构件在不同斑块中的均匀放置。研究结果表明，荆三棱的觅食行为与斑块大小具有相关性。     

主成分分析在三七连作土壤质量综合评价中的应用

三七(Panax notoginseng)的连作障碍效应严重,但其连作土壤质量研究尚属空白.本研究采用主成分分析结合聚类分析的方法对三七连作土壤质量进行综合评价,结果发现:微生物多样性指数、土壤真菌数量与比例及土壤P和K含量是决定土壤质量的关键因素.土壤质量综合评价指数(f值)可以较好地反映实际土壤质量,并能够在一定程度上反应三七植株的生长状况,是较为理想的土壤质量评价指标.     

我国认知行为疗法治疗失眠的疗效元分析

目的:探讨认知行为疗法对失眠症的治疗效果.方法:使用元分析方法,查阅中国期刊数据网,对符合标准的10篇原始文献使用元分析软件进行统计分析.结果:(1)异质性检验表明,所采用的10篇文献的数据均符合随机效应模型(p<0.001);(2)采用药物治疗和综合疗法(CBT+药物)治疗失眠症,两种方法治疗后PSQI的得分明显低于治疗前的得分(p<0.001);(3)综合组的治疗效果明显优于药物组(p<0.001).结论:认知行为疗法在失眠症的治疗中有极其重要的作用,并且综合疗(CBT+药物)的疗效明显优于药物组,值得临床推广应用.     

Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches

Gene expression data, in conjunction with information on genetic variants, have enabled studies to identify expression quantitative trait loci (eQTLs) or polymorphic locations in the genome that are associated with expression levels. Moreover, recent technological developments and cost decreases have further enabled studies to collect expression data in multiple tissues. One advantage of multiple tissue datasets is that studies can combine results from different tissues to identify eQTLs more accurately than examining each tissue separately. The idea of aggregating results of multiple tissues is closely related to the idea of meta-analysis which aggregates results of multiple genome-wide association studies to improve the power to detect associations. In principle, meta-analysis methods can be used to combine results from multiple tissues. However, eQTLs may have effects in only a single tissue, in all tissues, or in a subset of tissues with possibly different effect sizes. This heterogeneity in terms of effects across multiple tissues presents a key challenge to detect eQTLs. In this paper, we develop a framework that leverages two popular meta-analysis methods that address effect size heterogeneity to detect eQTLs across multiple tissues. We show by using simulations and multiple tissue data from mouse that our approach detects many eQTLs undetected by traditional eQTL methods. Additionally, our method provides an interpretation framework that accurately predicts whether an eQTL has an effect in a particular tissue.     

Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies

Meta-analysis is an increasingly popular tool for combining multiple different genome-wide association studies (GWASs) in a single aggregate analysis in order to identify associations with very small effect sizes. Because the data of a meta-analysis can be heterogeneous, referring to the differences in effect sizes between the collected studies, what is often done in the literature is to apply both the fixed-effects model (FE) under an assumption of the same effect size between studies and the random-effects model (RE) under an assumption of varying effect size between studies. However, surprisingly, RE gives less significant p values than FE at variants that actually show varying effect sizes between studies. This is ironic because RE is designed specifically for the case in which there is heterogeneity. As a result, usually, RE does not discover any associations that FE did not discover. In this paper, we show that the underlying reason for this phenomenon is that RE implicitly assumes a markedly conservative null-hypothesis model, and we present a new random-effects model that relaxes the conservative assumption. Unlike the traditional RE, the new method is shown to achieve higher statistical power than FE when there is heterogeneity, indicating that the new method has practical utility for discovering associations in the meta-analysis of GWASs.     

Effect size, confidence interval and statistical significance: a practical guide for biologists

Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.     

Meta-analysis of studies with missing data

Summary .  Consider a meta-analysis of studies with varying proportions of patient-level missing data, and assume that each primary study has made certain missing data adjustments so that the reported estimates of treatment effect size and variance are valid. These estimates of treatment effects can be combined across studies by standard meta-analytic methods, employing a random-effects model to account for heterogeneity across studies. However, we note that a meta-analysis based on the standard random-effects model will lead to biased estimates when the attrition rates of primary studies depend on the size of the underlying study-level treatment effect. Perhaps ignorable within each study, these types of missing data are in fact not ignorable in a meta-analysis. We propose three methods to correct the bias resulting from such missing data in a meta-analysis: reweighting the DerSimonian–Laird estimate by the completion rate; incorporating the completion rate into a Bayesian random-effects model; and inference based on a Bayesian shared-parameter model that includes the completion rate. We illustrate these methods through a meta-analysis of 16 published randomized trials that examined combined pharmacotherapy and psychological treatment for depression.     

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。     

Evaluating the Quality of Evidence from a Network Meta-Analysis

Systematic reviews that collate data about the relative effects of multiple interventions via network meta-analysis are highly informative for decision-making purposes. A network meta-analysis provides two types of findings for a specific outcome: the relative treatment effect for all pairwise comparisons, and a ranking of the treatments. It is important to consider the confidence with which these two types of results can enable clinicians, policy makers and patients to make informed decisions. We propose an approach to determining confidence in the output of a network meta-analysis. Our proposed approach is based on methodology developed by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group for pairwise meta-analyses. The suggested framework for evaluating a network meta-analysis acknowledges (i) the key role of indirect comparisons (ii) the contributions of each piece of direct evidence to the network meta-analysis estimates of effect size; (iii) the importance of the transitivity assumption to the validity of network meta-analysis; and (iv) the possibility of disagreement between direct evidence and indirect evidence. We apply our proposed strategy to a systematic review comparing topical antibiotics without steroids for chronically discharging ears with underlying eardrum perforations. The proposed framework can be used to determine confidence in the results from a network meta-analysis. Judgements about evidence from a network meta-analysis can be different from those made about evidence from pairwise meta-analyses.     

Environmental determinants of food-chain length: a meta-analysis

Food-chain length is an important character of ecological communities that affects many of their functional aspects. Recently, an increasing number of studies have tested the effects of productivity, disturbance, or ecosystem size on food-chain length in a variety of natural systems. Here we conduct a formal meta-analysis to summarize findings from these empirical studies. We found significant positive mean effects of productivity and ecosystem size but no significant mean effect of disturbance on food-chain length. The strength of mean effect sizes was not significantly different between productivity and ecosystem size. These results lend general support to previous theories predicting the effect of productivity and ecosystem size, but fail to support the prediction that disturbance shortens food chains. In addition, our meta-analysis found that the effect sizes of primary studies were significantly heterogeneous for ecosystem size and disturbance, but not for productivity. This pattern might reflect that ecosystem size and disturbance can affect food-chain length through multiple different mechanisms, while productivity influences food-chain length in a simple manner through energy limitation.     

Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis

We study recently developed nonparametric methods for estimating the number of missing studies that might exist in a meta-analysis and the effect that these studies might have had on its outcome. These are simple rank-based data augmentation techniques, which formalize the use of funnel plots. We show that they provide effective and relatively powerful tests for evaluating the existence of such publication bias. After adjusting for missing studies, we find that the point estimate of the overall effect size is approximately correct and coverage of the effect size confidence intervals is substantially improved, in many cases recovering the nominal confidence levels entirely. We illustrate the trim and fill method on existing meta-analyses of studies in clinical trials and psychometrics.     

Sample size calculation for multiple testing in microarray data analysis

Microarray technology is rapidly emerging for genome-wide screening of differentially expressed genes between clinical subtypes or different conditions of human diseases. Traditional statistical testing approaches, such as the two-sample t-test or Wilcoxon test, are frequently used for evaluating statistical significance of informative expressions but require adjustment for large-scale multiplicity. Due to its simplicity, Bonferroni adjustment has been widely used to circumvent this problem. It is well known, however, that the standard Bonferroni test is often very conservative. In the present paper, we compare three multiple testing procedures in the microarray context: the original Bonferroni method, a Bonferroni-type improved single-step method and a step-down method. The latter two methods are based on nonparametric resampling, by which the null distribution can be derived with the dependency structure among gene expressions preserved and the family-wise error rate accurately controlled at the desired level. We also present a sample size calculation method for designing microarray studies. Through simulations and data analyses, we find that the proposed methods for testing and sample size calculation are computationally fast and control error and power precisely.