meta分析范文展示

《Meta分析系列之十五_Meta分析的进展与思考》篇一Meta分析系列之十五_Meta分析的进展与思考Meta分析系列之十五：Meta分析的进展与思考一、引言Meta分析，作为一种综合分析多个独立研究结果的方法，已经在各个研究领域中得到了广泛的应用。

它通过对已发表或未发表的研究进行统计分析，综合各个研究的结果，从而得出更可靠、更准确的结论。

本文将就Meta分析的进展、应用领域、方法论思考以及未来发展方向进行探讨。

二、Meta分析的进展1. 历史回顾与早期发展Meta分析起源于20世纪40年代，最初主要用于医学领域。

随着研究的深入，其应用范围逐渐扩展到社会科学、心理学、教育学等多个领域。

早期Meta分析主要关注的是如何通过综合多个独立研究的结果来得出一个统一的结论，从而减少单个研究的局限性。

2. 现代Meta分析的进展随着统计技术和计算机技术的发展，现代Meta分析在方法论和实施上都有了显著的进步。

现代Meta分析不仅可以对定量数据进行综合分析，还可以对定性数据进行整合。

此外，现代Meta分析还注重对研究间的异质性进行评估，以更好地解释综合结果。

三、Meta分析的应用领域1. 医学领域在医学领域，Meta分析被广泛应用于药物疗效、疾病诊断、预防措施等方面的研究。

通过对多个临床试验的结果进行综合分析，可以更准确地评估药物的疗效和安全性，为临床决策提供依据。

2. 社会科学领域在社会科学领域，Meta分析被用于探讨各种社会现象和问题。

例如，通过综合多个研究的结果，可以更深入地了解教育政策、心理健康、社会结构等方面的问题。

四、方法论思考1. 研究的选择与质量评估在进行Meta分析时，如何选择合适的研究是关键。

除了关注研究的数量外，还要注重研究的质量。

质量评估是Meta分析的重要环节，通过对研究的设计、实施、结果等方面进行评估，可以确保所综合的研究具有较高的信度和效度。

2. 异质性的处理异质性是Meta分析中需要重点关注的问题之一。

《Meta分析系列之六_间接比较及网状分析》篇一Meta分析系列之六_间接比较及网状分析Meta分析系列之六：间接比较及网状分析一、引言Meta分析是一种统计技术，用于整合和分析多个独立研究的结果。

在医学、社会科学和许多其他领域中，Meta分析已成为一种重要的研究方法。

随着研究的不断深入，间接比较及网状分析（Network Meta-analysis, NMA）逐渐成为Meta分析的重要分支。

本文将详细介绍间接比较及网状分析的原理、方法、应用及高质量范文的撰写要点。

二、间接比较及网状分析的原理和方法1. 原理间接比较是一种通过比较多个研究之间的差异来评估不同干预措施效果的方法。

而网状分析则是在间接比较的基础上，通过构建网络图来展示不同干预措施之间的直接和间接比较结果。

NMA可以同时评估多个干预措施的效果，并比较它们之间的优劣。

2. 方法（1）文献检索与筛选：根据研究目的，制定明确的文献检索策略，筛选出符合纳入标准的文献。

（2）数据提取与整理：从选定的文献中提取所需数据，包括研究设计、样本量、干预措施、结局指标等，并整理成可用于NMA的格式。

（3）构建网络图：根据提取的数据，构建干预措施之间的网络图，展示各干预措施之间的直接和间接比较关系。

（4）统计分析：采用适当的统计模型进行NMA，计算各干预措施的效果估计值和置信区间。

（5）结果解读：根据统计结果，解读各干预措施的效果及优劣，并给出结论。

三、间接比较及网状分析的应用NMA在医学、社会科学等领域具有广泛的应用价值。

例如，在医学领域，NMA可以用于评估不同药物、手术和非药物治疗方法的效果，为临床决策提供依据。

在社会科学领域，NMA可以用于评估不同政策、教育方法和培训项目的效果，为政策制定和项目实施提供参考。

四、高质量范文的撰写要点1. 引言：简要介绍Meta分析和NMA的背景、目的和意义。

2. 研究方法：详细描述文献检索策略、纳入排除标准、数据提取和整理方法、NMA的统计模型等。

《Meta分析系列之五_贝叶斯Meta分析与WinBUGS软件》篇一Meta分析系列之五_贝叶斯Meta分析与WinBUGS软件Meta 分析系列之五：贝叶斯Meta分析与WinBUGS软件一、引言Meta分析作为一种综合分析多个独立研究结果的方法，在社会科学、医学、心理学等领域得到了广泛应用。

其中，贝叶斯Meta分析以其独特的统计方法和灵活的模型设定，在处理复杂数据时具有显著优势。

本文将详细介绍贝叶斯Meta分析的原理、方法和应用，并使用WinBUGS软件进行实例操作。

二、贝叶斯Meta分析概述1. 贝叶斯Meta分析原理贝叶斯Meta分析是基于贝叶斯统计方法，结合先验信息和样本信息，通过计算后验分布来评估效应量。

相较于传统的Meta分析方法，贝叶斯Meta分析在处理不确定性时更具优势，能更好地融合多个研究结果。

2. 贝叶斯Meta分析的优势（1）能够考虑样本之间的相关性；（2）能更全面地评估研究结果的不确定性；（3）可提供更为直观的效应量估计值。

三、WinBUGS软件介绍WinBUGS（Wine and Beaujolais/Gamma University BUGS）是一款常用的贝叶斯统计分析软件，广泛应用于生物医学、公共卫生等领域。

该软件支持多种模型设定和算法优化，可实现贝叶斯Meta分析等多种统计分析。

四、WinBUGS软件在贝叶斯Meta分析中的应用1. 数据准备与模型设定在WinBUGS软件中，首先需要准备好相关数据，并设定合适的模型。

这包括定义效应量、设置先验分布和设定随机效应模型等。

根据实际情况选择适当的模型是保证分析结果准确性的关键。

2. 运行程序与分析结果设定好模型后，使用WinBUGS软件进行计算和分析。

程序会生成后验分布、效应量估计值等统计量，并可绘制出相应的统计图。

通过分析这些结果，可以得出研究结论。

五、实例操作与结果解读以某项医学研究为例，我们将使用WinBUGS软件进行贝叶斯Meta分析。

《Meta分析系列之十五_Meta分析的进展与思考》篇一Meta分析系列之十五_Meta分析的进展与思考Meta分析系列之十五：Meta分析的进展与思考一、引言Meta分析，作为一种重要的文献综述和定量数据分析方法，自其诞生以来就广泛应用于多学科研究领域。

本篇文章旨在回顾Meta分析的进展，并对未来发展趋势进行思考。

二、Meta分析的起源与定义Meta分析最早源于科学哲学中对于研究的评价与总结。

在现代科研中，Meta分析主要指通过对已发表或未发表的研究进行统计整合，从而对特定研究问题得出更为准确和可靠的结论。

它强调的是对现有文献的二次分析，有助于对已有研究结果进行验证和扩展。

三、Meta分析的进展（一）研究方法的不断完善随着Meta分析的广泛应用，其研究方法也在不断发展和完善。

从最初的简单统计整合，到现在的多变量分析、贝叶斯Meta分析等，Meta分析的适用范围和深度都在不断扩大。

（二）跨学科应用Meta分析在多个学科领域都得到了广泛应用，如心理学、医学、社会学等。

它能够综合不同领域的研究成果，为解决复杂问题提供新的思路和方法。

（三）大数据与Meta分析的结合随着大数据时代的到来，Meta分析与大数据的结合成为了新的研究趋势。

通过对海量的文献数据进行Meta分析，可以更加准确地得出研究结论。

四、Meta分析的思考（一）可靠性问题尽管Meta分析能够综合多篇文献，提供较为准确的研究结论，但其在数据处理和分析过程中仍可能存在误差和偏倚。

因此，在运用Meta分析时，要重视研究设计、数据采集、分析方法等环节的可靠性问题。

（二）数据来源的多样性在进行Meta分析时，要充分考虑数据来源的多样性。

不同来源的数据可能存在差异，这可能会对研究结果产生影响。

因此，在整合数据时，要充分考虑数据来源的差异性和影响程度。

（三）伦理与法律问题在进行Meta分析时，需要关注伦理和法律问题。

例如，在处理涉及个人隐私和知识产权的数据时，要遵守相关法律法规和伦理规范。

《Meta分析系列之二_Meta分析的软件》篇一Meta分析系列之二_Meta分析的软件Meta分析系列之二：Meta分析的软件一、引言Meta分析作为一种综合性的文献研究方法，已经在多个学科领域中得到了广泛的应用。

然而，Meta分析的过程涉及到大量的数据整理、统计分析等工作，这就需要借助专业的软件来辅助完成。

本文将重点介绍Meta分析的软件，以及如何利用这些软件提高Meta分析的质量。

二、Meta分析软件概述1. 常用Meta分析软件目前，市面上有很多用于Meta分析的软件，如Comprehensive Meta Analysis (CMA)、Review Manager (RevMan)、Stata等。

这些软件在功能、操作等方面有所不同，但都能有效地支持Meta分析的过程。

2. 软件功能特点(1) Comprehensive Meta Analysis (CMA)：CMA是一款功能强大的Meta分析软件，支持多种统计模型，如固定效应模型、随机效应模型等。

同时，CMA还提供了丰富的图形工具，有助于研究者直观地了解分析结果。

(2) Review Manager (RevMan)：RevMan是Cochrane协作网推出的专门用于系统评价和Meta分析的软件。

RevMan操作简便，界面友好，适合初学者使用。

(3) Stata：Stata是一款强大的统计分析软件，也支持Meta分析。

Stata的语法较为复杂，但功能丰富，适合有一定统计学基础的研究者使用。

三、软件使用方法及注意事项1. 选择合适的软件在选择Meta分析软件时，应根据自己的需求、经验水平以及软件的功能特点进行综合考虑。

对于初学者，建议选择操作简便、界面友好的软件；对于有一定统计学基础的研究者，可以选择功能更强大、灵活性更高的软件。

2. 严格按照软件操作指南进行操作在使用Meta分析软件时，应严格按照软件操作指南进行操作，避免因操作不当导致分析结果出现偏差。

《Meta分析系列之十五_Meta分析的进展与思考》篇一Meta分析系列之十五_Meta分析的进展与思考Meta分析系列之十五：Meta分析的进展与思考一、引言随着科学研究的深入发展，Meta分析作为一种重要的统计方法，逐渐在各个领域中发挥着越来越重要的作用。

本文旨在探讨Meta分析的进展，以及在当代科学研究中的思考与应用。

二、Meta分析的概述Meta分析，即元分析，是一种利用统计方法对多个独立研究结果进行综合分析的方法。

它通过对不同研究结果进行量化评估和合并，从而得出更可靠、更全面的结论。

Meta分析在许多领域都有广泛的应用，如医学、心理学、社会科学等。

三、Meta分析的进展（一）方法论的完善随着Meta分析的不断发展，其方法论得到了进一步的完善。

在研究设计、数据采集、统计分析等方面，都出现了更多的方法和工具。

例如，通过系统评价和文献计量学的方法，可以更全面地收集和筛选相关研究；通过随机效应模型等统计方法，可以更准确地评估不同研究结果之间的异质性。

（二）应用领域的拓展Meta分析的应用领域不断扩大，不仅在医学、心理学、社会科学等领域得到广泛应用，还在生物学、计算机科学等领域得到尝试。

这表明Meta分析具有广泛的应用前景和潜力。

（三）与其他方法的结合Meta分析可以与其他统计方法相结合，如系统评价、网络元分析等，从而更好地解决实际问题。

此外，随着大数据和人工智能技术的发展，Meta分析与这些技术的结合也将为科学研究带来更多的可能性。

四、对Meta分析的思考（一）研究质量的保证在进行Meta分析时，需要保证所纳入的研究质量可靠。

这需要对研究的设计、数据采集、统计分析等方面进行全面评估。

同时，还需要注意研究间的异质性，避免因异质性过大而影响结果的可靠性。

（二）结果解读的准确性在进行Meta分析时，需要准确解读结果。

这需要对统计方法和结果进行深入理解，避免误解或误用。

同时，还需要注意结果的适用范围和局限性，避免过度解读或滥用结果。

The effect of fructose consumption on plasma cholesterol in adults: a meta-analysis of controlled feeding trials1,2,3Tao An4,5, Rong Cheng Zhang4,5, Yu Hui Zhang4, Qiong Zhou4, Yan Huang4, Jian Zhang4, *.4 State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China5Tao An and Rong Cheng Zhang contributed equally to this study.3 Supplemental Table 1 and supplemental Figures 1-4 are available as Online Supporting Material with the online posting of this paper at RUNNING TITLE: Fructose and cholesterolWORD COUNT: 5618; NUMBER OF FIGUREA: 3; NUMBER OF TABLES: 2 SUPPLEMENTARY MATERIAL: Online Supporting Materials: 5AUTHOR LIST FOR INDEXING: An, Zhang, Zhang, Zhou, Huang, Zhang1 The study was supported by the Ministry of Science and Technology of China with grant of the National High-tech Research and Development Program of China to Dr Jian Zhang.2 Author disclosures: T. An, R.C. Zhang, Y.H. Zhang, Q. Zhou, Y. Hung, J. Zhanghave no conflicts of interest.* To whom correspondence should be addressed. Mailing address: Heart FailureCenter, Cardiovascular Institute and Fuwai Hospital, Chinese Academy of MedicalSciences and Peking Union Medical College, 167 Beilishilu, Beijing, China; Zip code: 100000; Telephone number: 86-10-88396180; Fax number: 86-10-88396180; E-mail: Fwzhangjian62@PROSPERO REGISTRATION NUMBERS: CRD420120033511ABSTRACT2Fructose is widely used as a sweetener in production of many foods, yet the relation 3between fructose intake and cholesterol remains uncertain. We performed a systematic 4review and meta-analysis of human controlled feeding trials of isocaloric fructose 5exchange for other carbohydrates to quantify the effects of fructose on total 6cholesterol (TC), LDL cholesterol (LDL-C), and HDL cholesterol (HDL-C) in adult 7humans. Weighted mean differences were calculated for changes from baseline 8cholesterol concentrations by using generic inverse variance random-effects models. 9The Heyland Methodological Quality was used to assess study quality. Subgroup 10analyses and meta-regression were conducted to explore possible influence of study 11characteristics. Twenty-four trials (with a total of 474 subjects) were included in our 12meta-analysis. In an overall pooled estimate, fructose exerted no effect on TC, LDL-C 13and HDL-C. Meta-regression analysis indicated that fructose dose was positively 14correlated with the effect sizes of TC and LDL-C. Subgroup analyses showed that 15isocaloric fructose exchange for carbohydrates could significantly increase TC by 1612.97 mg/dL (95%CI: 4.66, 21.29; P = 0.002) and LDL-C by 11.59 mg/dL (95%CI: 174.39, 18.78; P = 0.002) at >100g fructose/d but had no effect on TC and LDL-C when 18fructose intake was ≤100g/d. In conclusion, very high fructose intake (>100g/d) 19could lead to significantly increase in serum LDL-C and TC. Larger, longer and 20higher-quality human controlled feeding trials are needed to confirm these results.21Key words: fructose, cholesterol, meta-analysis2223INTRODUCTION24Hyperlipidemia is a common risk factor for coronary heart disease (CHD), with 2544.4% of adults in the United States having abnormal TC values and 32% having 26elevated LDL-C levels (1). Compared to subjects with normal blood lipid, those with 27hyperlipidemia have a 3-fold risk of heart attacks (2). Lifestyle modification should be 28initiated in conjunction both primary and secondary prevention of CHD. More 29consideration exists as to what constitutes healthy eating.30Fructose is the most naturally occurring monosaccharide, and has become a major 31constituent of our modern diet. Fruit, vegetables, and other natural sources provide 32nearly one-third of dietary fructose, and two-thirds come from beverages and foods in 33the diets (eg, candies, jam, syrups, etc) (3). Fructose is preferred by many people, 34especially those with diabetes mellitus because of its low glycemic index (23% versus 35glucose 100%) (4). After intestinal uptake, fructose is mainly removed from the blood 36stream by the liver in an insulin-independent manner, and is used for intrahepatic 37production of glucose, fatty acids or lactate. Cross-sectional studies in human suggest 38that excessive fructose consumption can lead to adverse metabolic effects, such as 39dyslipidemia and increased visceral adiposity (5-7). The Dietary Guidelines for 40Americans, 2010, point out that it is lack of sufficient evidence to set a tolerable upper 41intake of carbohydrates for adults (8). Although The Candian Diabetes Association 42suggests consumption of no more than 60g of added fructose per day by people with 43diabetes for its triglyceride-raising effect (9), the threshold dose of fructose at which 44the adverse influence on cholesterol is controversial.45To determine the effect of fructose on cholesterol, a substantial number of clinical 46trials have been performed on adult humans with different health status (diabetic, 47obese, overweight, hyperinsulinemic, impaired glucose-tolerant and healthy). These 48trials used various intake levels of fructose and different protocols. Thus, it is difficult 49to reach a consistent conclusion across these studies. Therefore, we conducted a 50systematic review of the scientific literature and meta-analysis of controlled feeding 51trials to evaluate the effect of isocaloric oral fructose exchange for carbohydrates on 52cholesterol and to clarify the active factors of fructose.53Materials and Methods54This meta-analysis followed the Preferred Reporting Items for Systematic Reviews 55and Meta-analyses (PRISMA) criteria (10).56Search strategy.We searched PubMed (/pubmed; from 571966 to December 2012), Embase (; from 1966 to December 582012) and the Cochrane Library database () by using the 59following search terms: fructose and (lipemia or lipaemia or lipids or cholesterol or 60“total cholesterol”or “LDL cholesterol” or “HDL cholesterol”) in English. We also 61searched China National Knowledge Infrastructure () and Wangfang 62database () in Chinese according to the search strategy. The 63search was restricted to reports of trials on humans.64Study selection.All clinical trials using fructose and indexed within the above 65databases were collected. Two independent reviewers (T.A., R.C.Z) screened the 66abstracts and titles for initial inclusion. If this was not sufficient, full texts articles 67were obtained and reviewed by at least two independent reviewers (T.A., R.C.Z, Q.Z., 68Y.H.). The reference lists of retrieved articles also used to supplement the database.69Any disagreements were resolved through discussion. We included controlled feeding 70trials investigating the chronic effect of fructose on blood cholesterol, from both 71randomized and nonrandomized studies, if they met the following criteria: subjects 72must have been administered fructose for at least 2 weeks; studies investigated the 73effect of oral free (unbound, monosaccharide) fructose when compared with isocaloric 74control diet with another carbohydrate in place of fructose; studies were performed in 75human adults with either a parallel or crossover design; subjects in both experimental 76groups and control groups were instructed to consume isocaloric diets. If the study 77reported any comparisons, we included all such comparisons in the meta-analysis.78Data extraction and quality assessment.Two reviewers (T.A., R.C.Z) independently 79extracted relevant data from eligible studies. Disagreements were resolved by one of 80the two authors (Y.H.Z., J.Z.). These data included information on study features 81(author, year of publication, study design, randomization, blinding, sample size, 82comparator, fructose form, dose, follow-up and macronutrient profile of the 83background diet), participant characteristics (gender, age and healthy status) and 84baseline and final concentrations or net changes of total cholesterol, LDL-C and 85HDL-C. Data initially extracted were converted to system international unit (eg, TC: 1 86mmol/L converted to 38.6 mg/dL). For multi-arm studies, only intervention groups 87that met inclusion criteria were used in this analysis. If blood lipid concentrations 88were measured several times at different stages of trials, only final records of lipid 89concentrations at the end of the trials were extracted for this meta-analysis.90The quality of each study was assessed with the Heyland Methodological Quality 91Score (MQS) (11), generalized as follows: randomization; analysis; blinding; patient 92selection; comparability of groups at baseline; extent of follow up; treatment protocol;93co-intervention; outcomes. The highest score for each area was two points. Higher 94numbers represented a better quality (MQS≥8).95Data synthesis.Statistical analyses were performed with Stata software (version 11.0;96StataCorporation, TX, USA) and REVMAN software (version 5.2; Cochrane 97Collaboration, Oxford, United Kingdom). Separate pooled analyses were conducted 98by using the generic inverse variance random-effects models even where there was no 99evidence of between-study heterogeneity because these models give more 100conservative summary effect estimates in the presence of undetected residual 101heterogeneity than fixed-effects models. The different changes from baseline between 102fructose and carbohydrate comparators for total cholesterol, LDL cholesterol and 103HDL cholesterol were used to estimate the principle effect. We applied paired 104analyses to all crossover trials according to the methods of Elbourne and colleagues 105(12). Weighted mean differences of fructose consumption on cholesterol 106concentrations and corresponding 95% CIs were calculated. A 2-sided P value <0.05 107was set as the level of significance for an effect. The variances for net changes in 108serum cholesterol were only reported directly in two trials (29, 31). We calculate net 109changes for other studies by using the means±SDs cholesterol concentrations at 110baseline and at the end of intervention period (13). SDs were calculated from SEs 111when they were not directly given. If these data were unavailable, we extrapolated 112missing SDs by borrowing SDs derived from other trials in this meta-analysis (14). In 113addition, we assumed a conservative degree of correlation of 0.5 to impute the 114change-from-baseline SDs, with sensitivity analyses performed across a range of 115possible correlation coefficients (0.25 and 0.75) (13). For crossover trials in which 116only final measurements were included, the differences in mean final measurements 117were assumed on average to be the same as the differences in mean change scores 118(13). Inter-study heterogeneity was tested by the Cochrane’s Q-test (P < 0.1), and was 119quantified by the I2statistic, where I2 ≥ 50% was evidence of substantial heterogeneity. 120To explore the potential effects of factors on the primary outcomes and investigate the 121possible sources of heterogeneity, we performed meta-regressions and predefined 122subgroup analyses stratified by comparator, dose, study duration, randomization, 123health status, study design and study quality. As for studies used a range of fructose 124doses, the average doses calculated on the basis of the average reported energy intake 125or weight of participants (28.5 calories per kilogram of body weight). Sensitivity 126analyses were also performed according to the Cochrane Handbook for Systemic 127Review. Funnel plots and Egger’s linear regression test were conducted to detect 128publication bias.129RESULTS130Based on our search criteria, 1602 eligible studies were identified, and 1565 131studies were excluded on review of the titles and abstracts. The remaining 37 studies 132were retrieved and fully reviewed. Fifteen of these did not meet the inclusion criteria 133and were excluded in the final analysis. A total of 22 studies (providing data for 24 134trials) involving 474 subjects (15-36) were included in the meta-analysis 135(Supplemental Fig. 1, Table 1).136The reports of Koh and Reiser (22, 23) included two trials (bringing the total 137number of trials to 24). Eleven trials were randomized (17, 18, 20, 21, 25, 27-29, 31, 13834, 36). Nineteen trials used crossover (15-19, 21-32), and five used parallel designs 139(20, 33-36). As for the 19 cross-over trials, 10 trials have reported the washout period 140(16, 18, 22, 25, 27-31), 9 trials did not have washout period (15, 17, 19, 21, 23, 24, 26, 14132). The trials varied in size, from 8 to131 subjects. The mean age of trial participants 142ranged from 26.7 to 64.4 years. Seventeen trials (15, 17-23, 25, 27, 28, 30, 31, 34, 36) 143were performed in outpatient settings, 3 trials (26, 29, 32) in inpatient settings, and 4 144trials in both outpatient and inpatient settings (16, 24, 33, 35). Nine trials were 145conducted on diabetic subjects (19-21, 24-27, 29, 30), 8 trials in healthy subjects (17, 14618, 22, 23, 28, 31, 34, 35), 3 trials in overweight/obese subjects (32, 33, 36), 2 trials in 147hyperinsulinemic subjects (16, 23), 1 trial in those who were impaired 148glucose-tolerant (22), and 1 trial in subjects with type IV hyperlipoproteinaemia (HLP) 149(15). Background diets were 42-55% carbohydrate, 25-38% fat, and 13-20% protein. 150The carbohydrate comparators choose starch in 13 trials (15, 16, 21, 23-25, 27-30, 32, 15136), glucose in 6 trials (22, 31, 33-35), sucrose in 3 trials (17, 18, 26), and mixed 152carbohydrates in two trials (19, 20). Four trials used fructose in crystalline (16, 18, 20, 15321), 5 trials in liquid (19, 32-35), and 15 trials in mixed form (15, 17, 22-31). The 154reported mean baseline serum TC ranged from 170 to 230.8 mg/dl, LDL-C ranged 155from 90.7 to 157 mg/dl, and HDL-C ranged from 35.1 to 57.1 mg/dl. Nineteen trials 156reported the fructose intake among background diet was not different between the 157fructose and control groups, in which 15 trials reported the background fructose intake 158account for ≤3% of total energy (9 to 24g) (15-23, 29, 32, 33, 35), while 4 trials did 159not report the proportion of it (24, 25, 26, 34). Four trials used background fructose ≤3% 160(3.2 to 18g) of total energy in the control groups, but put total fructose into 161consideration in the fructose group (27, 28, 30-31). Only o ne trial reported less than 16220g (4.3 % of total energy) fructose was consumed among basal diet (36). The baseline 163values were not provided in 5 trials (19, 22, 23). The median fructose dose in the 164available trials included in our meta-analysis was 79.25 g/d (range: 30-182 g/d), and 165the duration varied from 2 to 26 weeks.166The quality scores of each study ranged from 6 to 9. Fifteen trials were classified 167as high quality (MQS≥8),and 8 trials were of low quality (17, 19, 26, 30, 32-35). 168Only three trials were blinded, one single-blinded (34) and 2 double-blinded (29, 35). 169Eight trials (19, 21, 24, 26-30) received industry funding. Three studies with four 170trials (15, 16, 22) did not report any information about financial conflicts of interest. 171Effect of fructose on cholesterol172Total cholesterol.Twenty-two trials (16-34, 36) reported the value of TC, and the 173pooled estimate was 2.47 mg/dL (95% CI: -3.04, 7.98; P = 0.38) without statistically 174heterogeneity (heterogeneity Chi2 = 28.14, I2= 25%, P = 0.14) (Fig. 1). The residual 175sources of heterogeneity were investigated by meta-regression models. Univariate 176meta-regression showed that the fructose dose was positively related to TC, even after 177adjusted for study duration and health status(regression coefficient = 0.18; 95% CI: 1780.06, 0.31, P = 0.008)(Table 2). The dose-response relation between fructose 179consumption and TC largely explained the residual heterogeneity of the effect. 180Subsequently, we stratified fructose dose ≤60, >60 to 100, and >100 as moderate, 181high, and very high, respectively, according to Candian Diabetes Association and 182reference ranges for fructose (9, 37, 38). Fructose could significantly increase TC by 18312.97 mg/dL (95%CI: 4.66, 21.29; P= 0.002) when fructose intakes were >100g/d 184but had no effect on TC if fructose was given lower than 100g. Predefined subgroup 185analyses were conducted by study characteristics (Supplemental Table 1). Sensitivity 186analyses according to possible correlation coefficients (0.25 and 0.75) and 187systematically removal of each individual trial did not alter the overall analysis and 188analyses stratified by dose.189LDL cholesterol.The mean change for LDL cholesterol in nineteen trials (15, 16, 18, 19020, 22, 23, 25-35) was 3.76 mg/dL (95% CI: -1.07, 8.6; P = 0.13) without statistically 191heterogeneity (heterogeneity Chi2 = 19.85, I2= 9%, P = 0.34) (Fig. 2). The residual 192sources of heterogeneity were investigated by meta-regression models. Univariate 193meta-regression showed that the fructose dose was positively related to LDL-C, even 194after adjusted for comparators, study duration and health status(regression coefficient 195= 0.15; 95% CI: 0.03, 0.28, P = 0.02)(Table 2). The dose-response relation between 196fructose consumption and LDL-C largely explained the residual heterogeneity of the 197effect. We stratified fructose dose according to CDA and reference ranges for fructose 198(9, 37, 38). Fructose intake >100g/d could significantly increase LDL-C by 11.59 199mg/dL (95%CI: 4.39, 18.78; P= 0.002). Predefined subgroup analyses were 200conducted by other study characteristics (Supplemental Table 1). Sensitivity analyses 201across possible correlation coefficients (0.25 and 0.75) did not alter the overall 202analysis and analyses stratified by dose. The removal of Cybulska et al resulted in a 203significant LDL-C-raising effect in the overall analysis (P = 0.03).204HDL cholesterol.The result of HDL cholesterol was calculated based on 24 trials 205(15-36), the mean difference was -0.56 mg/dL (95% CI: -2.05, 0.93; P = 0.46) without 206heterogeneity (heterogeneity Chi2 = 21.85, I2= 0%, P= 0.53) (Fig. 3). 207Meta-regression analysis did not show significant effect modifier of HDL-C. 208Predefined subgroup analyses were conducted by study characteristics (Supplemental 209Table 1). Sensitivity analyses according to possible correlation coefficients (0.25 and 2100.75) and systematically removal of each individual trial did not alter the overall 211analysis.212Publication bias213Funnel plots and Egger’s test indicated no significant publication bi as in the 214meta-analyses of TC, LDL cholesterol, and HDL cholesterol (TC Egger’s test: P= 2150.881; LDL cholesterol Egger’s test: P= 0.815; HDL cholesterol Egger’s test: P= 2160.484) (Supplemental Figs. 2-4).217DISCUSSION218This meta-analysis of 24 controlled feeding trials with 477 subjects found no 219effect on TC, LDL-C and HDL-C when fructose was substituted for other 220carbohydrates. Residual heterogeneity was detected by meta-regression for this 221outcome that fructose dose was positively correlated with the effect sizes of TC and 222LDL-C.223The present meta-analysis is consistent with a prospective 2-year trial on chronic 224effect of fructose from Turku sugar studies XI, which did not report any change in 225cholesterol for those individuals who consumed more than 100g fructose/d (39). 226Aeberli et al reported another prospective, randomized, 3-week controlled crossover 227trial in which healthy young men were fed 80 g/d free fructose, and found a 228significant atherogenic LDL subclass distribution (40). However, there was an average 229of 34g combined fructose consumed among basal foods in this study, which meant 230subjects consumed fructose over 110g/d. The median dose of fructose available in our 231meta-analysis was ≈79.25 g/d, it was higher than 90th percentile (78 g/d) and lower 232than 95th percentile (87 g/d) in the United States, reported by the National and Health 233and Nutrition Examination Survey III (41). As for subjects with diabetic mellitus, 234Sievenpiper et al did not report cholesterol-raising effect if the fructose dose was >60 235g/d (median: 97.5 g/d) in their meta-analysis (42). The result of our study and 236intervention trials may be supported the idea that fructose did not increase cholesterol 237for the subjects with generalizable levels of exposure.238The results of subgroup analyses showed that the effects of fructose intake on TC 239and LDL-C were significant as the fructose dose > 100g/d. An intake of 100g/d is 240approximately equal to 400kcal/d or 20% of energy intake for a sedentary person with 241an energy requirement of 2000 kcal/d. The doses for cholesterol-raising effect account 242for less than 10 percent of intake in males and females aged 19 to 22 years, the group 243with the highest level of exposure in the United States (41). Another study found that 244the upper quintile of Americans consume more than 110g fructose daily as added 245sugar or as high-fructose corn syrup (43). Although a small number of people 246consume fructose at very high dose, it is necessary to advise them to change their 247lifestyle.248The dose-dependent effect on triglyceride was also reported in a recent 249meta-analysis that concluded the same dose threshold of 100g/d for a 250triglyceride-increasing effect of fructose on fasting triglyceride level in adult humans 251(38). For healthy subjects who consumed 150g of fructose/day, endogenous 252cholesterol synthesis and the fat content of viscera and liver have been shown to 253increase (44). All evidences have proved that fructose is proposed to have adverse 254effects at very high or excessive doses. The mechanism of the cholesterol increase by 255fructose might be due to increased levels of advanced glycation end products, which 256cause damage to LDL and make it poorly recognized by lipoprotein receptors and 257scavenger receptors (45). Furthermore, excess exposure to fructose can damage the 258function of adipocytes and may reduce the recycling of cholesterol extracted from 259serum LDL. Studies have shown that elevated uric acid might contribute to LDL-C 260increases, and this effect can be reduced by allopurinol (46).261Based on the composition of added sugars in the United States where the fructose: 262glucose ratio is close to 0.43, and the NHANES 1999–2004 estimates (41), the 263increase of fructose consumption is always accompanied with an increase in total 264energy intake. Persons consuming >100g/d of sugars are potentially eating in excess 265of their energy requirement (47), and then overweight and obesity could result. So we 266can not suggest that it is safe to only limit fructose to <100g/d in coronary heart 267disease management and prevention. It may need to take into account the other 268components of foods that accompany the fructose. This dose threshold effects on TG 269and LDL-C can only help better inform nutritional guidance and avoid inappropriate 270marketing of carbohydrates.271Our meta-analysis did not show significant effect of fructose on HDL-C. 272However, Perez-Pozo et al (46) reported a significant HDL-C-lowing effect in 74 273adult men fed with 200g fructose/d in a randomized, 2-week crossover trial, 274suggesting that excessive fructose dose intake can also affect HDL-C. Further trials 275are needed to find the threshold of fructose on HDL-C.276There are several limitations to our work. First, many trials had a relatively small 277sample size, and most of them were funded by industry which can affect the quality of 278studies. Second, the change of fructose in the background diet can affect the practical 279utility of the outcomes of meta-analyses. However, most of trials used the background 280diet with ≤ 3% of total energy derived from fructose (15-23, 27-33, 35), others trials 281did not report the proportion of fructose in the background (24, 25, 26, 34). It was 282hard to make sure the dose of background fructose in every trial. Third, the data 283provided by Reiser et al (23) must be interpreted with caution. Although this study 284met all of our inclusion criteria, they choose a low P:S (polyunsaturated : saturated) 285rate of the fat as the background diet, which might change the metabolism of fructose 286as diets high in saturated fatty acids can enhance intestinal fructose absorption (48). 287Fourth, some of included trials lack test statistics, baseline values and SDs. We 288overcame these problems according to the methods proposed by Cochrane Handbook 289for Systematic Reviews of Interventions. Finally, it is difficult to differentiate effects of 290other modifiers, such as exercise and age, from those included trials. These factors can 291also influence the final result. Fructose can indeed be metabolized during exercise, 292and the rate of metabolism is different between exercise and sedentary lifestyle. Most 293of participants were requested to follow a designed regiment at home, but it is not 294easy to maintain the activity intensity. On the plus size, the age of participants in our 295meta-analysis ranged from 18 to 72 years old. Evidence from animal experiments 296shows that fructose absorption may affected by age, as older rats showed decreased 297fructose absorption (49). However, no human trial has been done to assess the 298difference in fructose effect among different age groups. Therefore, further studies 299should attempt to limit or isolate the degree of heterogeneity present in the study 300population to better assess the effect of age.301In conclusion, our meta-analysis shows that fructose used as a sweetener in 302isocaloric exchange for other carbohydrates has significant increasing effects on TC 303and LDL-C in individuals with very high fructose (>100g). This effect seems not to be 304dose-dependent when fructose is given at moderate or high dose of fructose (<100g). 305Further studies should concentrate on larger, longer and higher-quality human 306controlled feeding trials, which provide a better assessment of the effect of fructose on 307cholesterol.308Acknowledgements309Tao An, Rong Cheng Zhang and Jian Zhang designed the research; Tao An, Rong 310Cheng Zhang, Yu Hui Zhang and Jian Zhang preformed the research; Tao An and 311Rong Cheng Zhang summarized the data and had primary responsibility for the 312accuracy of the analysis; Rong Cheng Zhang wrote the manuscript. All the authors 313had full access to the data. None of the authors declared a conflict of interest.314。

《Meta分析系列之六_间接比较及网状分析》篇一Meta分析系列之六_间接比较及网状分析Meta分析系列之六：间接比较及网状分析一、引言Meta分析作为一种综合分析多个研究结果的方法，被广泛应用于医学、社会科学等多个领域。

随着研究的不断深入，间接比较及网状分析作为Meta分析的重要组成部分，其重要性日益凸显。

本文将介绍间接比较及网状分析的原理、方法、应用及其在实践中的高质量范式的撰写。

二、间接比较及网状分析的原理和方法1. 原理间接比较是指在多个研究之间无法直接进行比较时，通过某种方法对研究结果进行间接比较的过程。

网状分析则是一种基于贝叶斯统计的复杂网络模型，用于评估多个干预措施之间的直接和间接比较。

2. 方法（1）间接比较在进行间接比较时，首先需要确定各个研究的共同变量或效应指标。

然后，通过转换或标准化等方法，将不同研究的结果调整到同一尺度上，以便进行比较。

此外，还需要考虑研究间的异质性，通过统计学方法对异质性进行评估和处理。

（2）网状分析网状分析主要利用贝叶斯网络模型，对多个干预措施进行全面的评估和比较。

首先需要构建一个网络模型，包括节点（各干预措施）和边（各干预措施之间的直接和间接比较）。

然后根据数据收集的结果和网络模型的参数设置，计算每个干预措施与其他措施的比较效果，最后通过概率排名等统计方法，对所有干预措施进行比较和评估。

三、间接比较及网状分析的应用1. 在临床医学中的应用间接比较及网状分析在临床医学领域有着广泛的应用。

例如，针对不同的治疗方法或药物，通过Meta分析和网状分析可以评估各种治疗方法的疗效和安全性，为临床医生提供参考依据。

此外，在药物研发过程中，也可以通过间接比较和网状分析对不同药物进行综合评估和比较。

2. 在社会科学中的应用在社会科学领域，间接比较及网状分析同样具有广泛的应用价值。

例如，在政策评估中，可以通过Meta分析和网状分析对不同政策的效果进行综合评估和比较，为政策制定提供参考依据。

《Meta分析系列之十五_Meta分析的进展与思考》篇一Meta分析系列之十五_Meta分析的进展与思考Meta分析系列之十五：Meta分析的进展与思考一、引言Meta分析，作为一种强大的统计工具，已广泛应用于各个研究领域。

自其诞生以来，Meta分析在整合、比较和综合不同研究结果方面发挥了重要作用。

本文将探讨Meta分析的进展、当前的应用领域以及面临的挑战与思考。

二、Meta分析的进展1. 定义与发展Meta分析最初用于医学领域，通过对之前研究结果进行再次分析，综合各个研究结果来得到更为可靠的结论。

随着统计方法和计算机技术的进步，Meta分析逐渐扩展到其他领域，如社会科学、心理学、教育学等。

2. 统计方法的进步早期的Meta分析主要依赖于固定效应模型和随机效应模型。

随着研究的深入，越来越多的统计方法被引入到Meta分析中，如贝叶斯Meta分析、多元回归Meta分析等。

这些新方法使得Meta 分析能够更好地处理异质性、考虑多个变量因素等复杂问题。

3. 技术的应用随着大数据和人工智能的兴起，Meta分析在技术应用方面也取得了显著的进展。

通过运用先进的算法和软件工具，可以快速地收集、整理、分析和解释大量文献数据，从而提高Meta分析的效率和准确性。

三、Meta分析的应用领域1. 医学领域医学领域是Meta分析的主要应用领域之一。

通过对医学文献进行Meta分析，可以综合不同研究结果，评估某种药物或治疗方法的效果，为临床实践提供参考依据。

2. 社会科学领域在社会科学领域，Meta分析被广泛应用于心理学、教育学、社会学等学科的研究中。

通过对不同研究的综合分析，可以揭示某一现象或问题的本质和规律。

3. 其他领域除了医学和社会科学领域外，Meta分析还应用于其他领域，如经济学、管理学等。

在这些领域中，Meta分析可以帮助研究者整合不同研究结果，为决策提供科学依据。

四、面临的挑战与思考1. 数据质量与选择偏倚在进行Meta分析时，数据的质量和选择偏倚是两个重要的问题。

《Meta分析系列之二_Meta分析的软件》篇一Meta分析系列之二_Meta分析的软件Meta分析系列之二：Meta分析的软件的高质量范文一、引言Meta分析是一种统计方法，用于综合多个独立研究的结果，以提供更全面、更准确的结论。

随着科研工作的不断发展，Meta 分析在各个领域的应用越来越广泛。

本文将重点介绍Meta分析的软件，探讨其功能、特点及使用方法，以期为读者提供有价值的参考。

二、Meta分析软件概述Meta分析软件是用于执行Meta分析的计算机程序，其功能包括数据管理、效应量计算、异质性检验、合并效应量等。

目前市面上常见的Meta分析软件包括RevMan、Stata、R语言等。

这些软件具有不同的特点，适用于不同的研究需求。

三、常见Meta分析软件介绍1. RevManRevMan是一款由Cochrane Collaboration开发的免费软件，主要用于系统评价和Meta分析。

其优点在于操作简便、界面友好，支持多种类型的效应量计算和模型选择。

然而，RevMan的功能相对有限，对于复杂的Meta分析可能不够灵活。

2. StataStata是一款功能强大的统计分析软件，也支持Meta分析。

其优点在于具有丰富的统计功能和强大的数据处理能力，可以处理各种复杂的数据结构和效应量模型。

同时，Stata的编程语言易于学习，方便用户进行定制化开发。

3. R语言R语言是一种开源的统计分析语言，具有广泛的应用领域。

在Meta分析方面，R语言具有强大的功能和灵活性，支持各种类型的效应量计算和模型选择。

此外，R语言还具有丰富的第三方包和社区支持，方便用户获取帮助和解决问题。

四、Meta分析软件的使用方法1. 数据准备：收集相关研究的数据和资料，包括研究设计、样本信息、效应量等。

2. 软件选择：根据研究需求和数据分析能力选择合适的Meta 分析软件。

3. 数据导入：将数据导入到所选软件中，进行数据管理和清洗。

4. 效应量计算：根据研究类型选择合适的效应量指标，如OR、RR、MD等。