BMJ的一篇meta分析范文分享.doc
《2024年Meta分析系列之十五_Meta分析的进展与思考》范文
《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。
《2024年Meta分析系列之十五_Meta分析的进展与思考》范文
《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工具，已广泛应用于科学研究领域。
自其诞生以来，就为学者们提供了一个更为精准和系统的分析手段，来对相关领域内的众多研究进行汇总、评价和比较。
在过去的几十年里，随着科技和方法的不断进步，Meta分析也得到了长足的发展。
本文旨在探讨Meta分析的进展、现状以及未来发展方向，以期为相关研究提供参考。
二、Meta分析的进展（一）方法论的完善随着Meta分析的广泛应用，其方法论也在不断完善。
从最初的简单统计合并到现在的多层次模型、贝叶斯分析等复杂方法，Meta分析的精确性和可靠性得到了显著提高。
此外，针对特定类型的研究设计（如诊断试验、干预研究等），也发展出了相应的Meta分析方法。
（二）数据来源的扩展随着互联网和数据库技术的快速发展，Meta分析的数据来源得到了极大的扩展。
除了传统的学术期刊、会议论文等，现在还可以从网络资源、政府报告等获取数据。
同时，大数据和人工智能技术的应用也为Meta分析提供了更为丰富的数据来源。
（三）应用领域的拓展Meta分析的应用领域已经从最初的医学领域扩展到了社会科学、教育学、心理学等多个领域。
这些领域的学者们通过Meta分析对大量相关研究进行综合评价，为政策制定、教育实践等提供了有力的依据。
三、当前Meta分析的挑战与思考（一）数据质量问题随着数据来源的扩展，数据质量问题也日益凸显。
在Meta分析中，数据的质量直接影响到结果的准确性和可靠性。
因此，如何确保数据的真实性和准确性是当前Meta分析面临的重要挑战。
（二）方法论的局限性虽然Meta分析的方法论在不断完善，但仍存在一些局限性。
例如，对于某些特殊类型的研究设计（如定性研究、混合方法研究等），现有的Meta分析方法可能无法完全适用。
《2024年Meta分析系列之十五_Meta分析的进展与思考》范文
《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。
《2024年Meta分析系列之十五_Meta分析的进展与思考》范文
《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。
meta分析范文展示
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。
系统评价与meta分析的报告范文
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meta分析 论文
meta分析论文以下是一篇关于meta分析的论文的例子：标题：A Meta-analysis of the Efficacy of Cognitive-Behavioral Therapy for Depression in Adolescents摘要：该研究旨在通过meta分析评估认知行为疗法（CBT）在青少年抑郁症治疗中的疗效。
我们检索了包括英文和中文在内的巴基斯坦、印度和中国等地的相关数据库，并共纳入了15项研究。
结果显示，CBT在青少年抑郁症治疗中具有显著的疗效，具体表现为抑郁指标的显著下降，自我报告的心理健康水平的提高，以及生活质量的改善。
进一步的亚组分析发现，CBT的疗效在不同性别、年龄和治疗形式的青少年之间没有显著差异。
然而，随机对照试验的质量与CBT的疗效之间存在一定程度的关联，高质量的研究显示出更好的治疗效果。
本研究的结果强调了CBT在青少年抑郁症治疗中的重要性，并提供了进一步研究的建议。
关键词：meta分析、认知行为疗法、青少年、抑郁症、疗效引言：青少年抑郁症是一种常见的精神疾病，对患者的生活质量和学业成就产生负面影响。
虽然有多种治疗方法可供选择，但研究结果不一致，缺乏一致的证据支持。
因此，本研究旨在通过meta分析综合评估CBT在青少年抑郁症治疗中的疗效。
方法：我们检索了PubMed、PsycINFO、Cochrane图书馆和中国知网等数据库，以纳入符合包括青少年、抑郁症和认知行为疗法等关键词的研究。
最终，共纳入了15项符合纳入标准的研究。
结果：该meta分析显示，CBT对青少年抑郁症的治疗具有显著疗效（汇总效应大小为0.65，95%置信区间为0.45-0.85），表现为抑郁指标的显著下降，自我报告的心理健康水平的提高，以及生活质量的改善。
亚组分析结果显示，CBT的疗效在不同性别、年龄和治疗形式的青少年之间没有显著差异（P>0.05）。
然而，随机对照试验的质量与CBT的疗效存在正相关（P<0.05），高质量的研究显示出更好的治疗效果。
《2024年Meta分析系列之十五_Meta分析的进展与思考》范文
《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分析文字版
AbstractBackground: Whether depression causes increased risk of the development of breast cancer has long been debated. We conducted an updated meta-analysis of cohort studies to assess the association between depression and risk of breast cancer. Materials and Methods: Relevant literature was searched from Medline, Embase, Web of Science (up to April 2014) as well as manual searches of reference lists of selected publications. Cohort studies on the association between depression and breast cancer were included. Data abstraction and quality assessment were conducted independently by two authors. Random-effect model was used to compute the pooled risk estimate. Visual inspection of a funnel plot, Begg rank correlation test and Egger linear regression test were used to evaluate the publication bias. Results: We identified eleven cohort studies (182,241 participants, 2,353 cases) with a follow-up duration ranging from 5 to 38 years. The pooled adjusted RR was 1.13(95% CI: 0.94 to 1.36; I2=67.2%, p=0.001). The association between the risk of breast cancer and depression was consistent across subgroups. Visual inspection of funnel plot and Begg’s and Egger’s tests indicated no evidence of publication bias. Regarding limitations, a one-time assessment of depression with no measure of duration weakens the test of hypothesis. In addition, 8 different scales were used for the measurementof depression, potentially adding to the multiple conceptual problems concerned with the definition of depression. Conclusions: Available epidemiological evidence is insufficient to support a positive association between depression and breast cancer.IntroductionDepression is highly prevalent in the general population, and it is estimated that 5.8% of men and 9.5% of women will experience a depressive episode in a 12-month period. The lifetime incidence of depression has been estimated at more than 16% in the general population (World Health Organization, 2001; Kessler et al., 2003; World Health Organization, 2008). Breast cancer is by far the most commom cancer in women (International Agency for Research on Cancer, 2008), the global burden of breast cancer measured by incidence and mortality is substantial and on the increase (Benson et al., 2012). There are an estimated 1.5 million cases diagnosed annually and almost 0.5 million died from this disease, representing 14% of female cancer deaths in the worldwide (Jemal et al., 2011; Benson et al., 2012). Many factors have been shown to be associated with the occurrence of breast cancer, such as having a first degree relative with breast cancer, bearing the first child at a late age, alcohol consumption and long term use of menopausal estrogen replacement therapy (Kampert et al., 1988; Gail et al., 1989;Slattery et al., 1993). However, it has long been debated that whether depression is an increased risk of the development of breast cancer. Depression may affect the endocrine and immune function (Kowal et al., 1955; Miller et al., 1993), which may have influence on cancer initiation and progression, including breast cancer. Importantly, women themselves widely believed that depression was a risk factor in the development of their breast cancer (Mitchell et al., 1995). However epidemiology evidences on the association between depression and breast cancer incidence are mixed and inconclusive.A great many of studies have assessed the association between depression and subsequent risks of breast cancer. A previous meta-analysis (Oerlemans et al., 2007) focusing on breast cancer pooled results from 7 prospective studies published before 2003 as a secondary analysis and reported a pooled relative risk estimated of 1.59 (95% confidence intervals, 0.74-3.44). Since then some cohort studies have been published, which provide stronger evidence of the association between depression and breast cancer. Therefore, we conducted a meta-analysis of cohort studies to describe the association between depression and risk of breast cancer.Materials and MethodsSearch strategyWe conducted a systematic literature search (up toApril 2014) of Medline, Embase, Web of Science for studies describing the association between depression and breast cancer. We used the following terms “depression”or “depressive disorder” or “major depressive disorder”or “depressive symptoms” and “breast cancer” or “breast carcinoma” combined with “cohort study” or “prospective study” or “follow-up study” or “longitudinal study”.In addition, studies from reference lists of all relevant publications and reviews were searched to identify potential pertinent studies.Study selectionStudies meeting the following criteria would be included in this meta-analysis: i) the study was a cohort design (prospective cohort or historical cohort); ii) the exposure was depression symptoms or depressive disorder which were measured by self-reported scales or structured clinical interview or clinician diagnosis; iii) the endpoint was diagnosis or report of breast cancer, all participants were free of any subtypes of cancer at the beginning of the study; iv) the study reported the RR or hazard risk(HR) with corresponding 95% CIs for the association between depression and breast cancer; and v) study was publishedin English. If multiple independent published reports were from a same cohort, only the latest one was included. Study selection was independently performed by two authors (S.H.L and D.X.X) and conflicts were resolved through discussion with the third reviewer (L.Z.X).Data extractionWe extracted the following information fromeach retrieved article: name of the first author, yearof publication, study location, characteristics of study population at baseline, duration of follow-ups, sample size, numbers of cases, depression and breast cancer measurements, adjusted effect estimate and corresponding 95% CIs, and variables used in multivariable analysis. Quality assessment was performed according to the Newcastle-Ottawa quality assessment scale for cohort studies (Wells et al., 2006) by two investigators (S.H.L and D.X.X). This scale allocates a maximum of nine points for quality of selection (0-4 points), comparability (0-2 points), exposure and outcome of study participants (0-3 points). The two authors discussed the implementationof this assessment tool and agreed on a method of implementation before their independent assessments ofstudies. The level of agreement between the two reviewers was calculated by another investigator (L.Z.X).Statistical analysisThe RRs were used as the common measure of association across studies, and the hazard ratios (HRs) were considered equivalent to RRs. Forest plot was produced to visually assess the RRs and corresponding 95% CIs across studies. Statistical heterogeneity across studies was estimated by I2 statistic. I2 values of 25%, 50%, 75% are regarded as cut-off points for low, moderate and 2003). The RRs were pooled using the fixed-effect modelif no or low heterogeneity was detected, or random-effect model otherwise (DerSimonian et al., 1986). In sensitivity analyses, we conducted leave-one-out analysis (Wallaceet al., 2009) for each study to examine the magnitude of influence of each study on pooled risk estimates. Subgroup analyses for study location, number of participants and cases, follow-up time, exposure measurement, smokingor alcohol drinking and study quality were conductedto examine the robustness of the primary results. Visual inspection of a funnel plot and Begg rank correlation test, Egger linear regression test (Begg et al., 1994;Egger et al., 1997) were used to evaluate the potential publication bias. The Duval and Tweedie nonparametric trim-and-fill procedure(Duval et al., 2000) was used to further assess the possible effect of publication bias. All statistical analyses were performed with STATA version 11.0 (StataCorp, College Station, Texas, USA). All tests were two sided with a significance level of 0.05.ResultsEligible studiesTotally 1705 articles were identified from the Medline, Embase, Web of Science. After the first round of screening based on titles and abstracts with aforementioned criteria, 1682 articles were excluded. Examining the articles remained in more details, nine articles (Hahn et al.,1988; Jacobs et al., 2000; Dalton et al., 2002; Nyklicek et al., 2003; Goldacre et al., 2007; Gross et al., 2010; Chen et al., 2011; Liang et al., 2011; Lemogne et al., 2013) met the inclusion criteria. The detailed reasons for exclusion were shown in Figure 1. Besides, one article (Schuurman et al., 2001) was found from the previous meta-analysis (Oerlemans et al., 2007) and one (Knekt et al., 1996) was identified by searching the reference lists. In total, elevenarticles were included in this meta-analysis.Study characteristicsCharacteristics of the eleven articles were showedin Table 1. These studies were published between 1988 and 2013. The sample size of studies varied from 1,533to 57,320, with a total of 182,241, and the number of breast cancer cases ranged from 20 to 728, with a totalof 2,353. With regard to study location, three studies were conducted in the USA, two studies in Taiwan, twoin Netherlands, one in France, one in the UK, one in Denmark, and one in Finland. In four of eleven studies, depression was measured by self-reported scales which were the Center for Epidemiologic Studies Depression Scale (CES-D), General Health Questionnaire (GHQ), Minnesota Multiphasic Personality Inventory(MMPI), and Ediburgh Depression Scale (EDS). Two studies used the Diagnostic Interview Schedule (DIS) and one used International Classification of Health Problems in Primary Care (ICHPPC) to define depression. The other four studies defined depression according to the International Classification of Disease, Ninth Revision, Clinical Modification or International Classification of Disease,Eighth Revision, Clinical Modification (ICD-9-CM or ICD-8-CM). The outcome of studies was ascertained by medical records or death certificates in seven studies, by self-report in two studies, and by combining self-report with medical records in the rest two studies. The eleven articles were assessed and were of moderate quality with a mean score of 6.9 (ranging from 6-8).All the included studies provided adjusted RRs. The major confounding factors adjusted included age, family history of breast cancer, cigarette smoking, alcohol intake, obesity, social status, and complications.Association between depression and risk of breast cancer The association between depression and breastcancer risk was shown in Figure 2. The majority of allthe eleven studies indicated a positive trend between depression and breast cancer (RR>1), but only two of them were statistically significant. At the same time one article (Nyklicek et al., 2003) reported that depression could reduce the risk of breast cancer in middle-aged women. With a moderate to high heterogeneity (I2=67.2%, p=0.001), the pooled analysis from random-effect model revealed that depression was not associated with breastcancer risk (RR,1.13; 95% CI 0.94 to 1.36).Subgroup analyses and sensitivity analysesTable 2 showed the results of subgroup analyses. We conducted subgroup analyses by study characteristics, such as study locations, number of study of participants and cases, duration of follow-up, exposure levels and study quality, while the results were not statistically significant. In addition, we conducted subgroup analyses according to the results whether or not adjusted by alcohol consumption or smoking, and neither alcohol consumption nor smoking altered the association.one showed that Jacobs et al’s study (Jacobs et al., 2000) and Goldacre et al’s study (Go ldacre et al., 2007) imposed the largest influence on the results. The pooled RRs were 1.24 (95%CI: 0.95-1.61) and 1.06 (95%CI 0.92-1.22) after excluding the two studies, respectively.Publication biasVisual inspection of funnel plot revealed some asymmetry (see supplementary Figure 1A). However,the Begg rank correlation test, Egger linear regressiontest provide no evidence of substantial publication bias (Begg’s test Z=1.25, p=0.213; Egger’s test t=-0.39,p=0.709). A sensitivity analysis using the trim-and-fill method was performed with 3 imputed studies, which produced a symmetrical funnel plot (see supplementary Figure 1B). The pooled RR incorporating the three hypothetical studies was smaller than the original results, but it still did not reach the statistically significant (RR, 1.04; 95% CI, 0.84-1.27).DiscussionThe study results were derived from eleven cohort studies which reported association between depression and risk of breast cancer. In all, our meta-analysis involved 2,353 cases of breast cancer and 182,241 participants.No significant association between depression and risk of breast cancer was found (RR, 1.13; 95%CI, 0.94 to 1.36) after adjustment for potential confounders. Furthermore, the association between depression and breast cancer persisted across subgroup analyses.Taking into account the impact of ethnic and geographic on the incidence of breast cancer, subgroup analyses by locations (European countries vs. USA vs. Taiwan) were conducted but no significant difference was found. As we know, different levels of exposure mayhave different effects on the study outcome. Therefore,we conducted subgroup analysis by exposure levels (depression symptoms vs. depressive disorder) which showed no statistically significant association between depression and breast cancer risk. Given that a long period was required to develop a detective tumor, subgroup analysis by the duration of follow-up were conductedand the results were not statistically significant as well, though the RR was elevated in the cohorts of more than10 years of follow-up. There were studies identifiedthat depression individuals may engage more unhealthy behaviors that predispose them to further onset of cancer, such as smoking, alcohol consumption, lack of physical activity (Son et al., 1997; Strine et al., 2008). But the subgroup analyses according to the results that whetheror not adjusted by smoking and alcohol consumption didnot find significant association.A meta-analysis conducted by Marjolein EJ Oerlemanset al. (2007) in 2007 investigated the relationship between depression and overall cancer risk. The previous metaanalysis also identified association between depressionand breast cancer as a secondary analysis. The secondaryanalysis included seven prospective studies which involved 111756 participants and 1601 cases and reported no significant association (RR, 1.59; 95%CI, 0.74-3.44). Our meta-analysis, with four more cohort (Goldacre et al., 2007; Chen et al., 2011; Liang et al., 2011; Lemogne et al., 2013) studies and one update study (Gross et al., 2010), demonstrates no evidence of association between depression and breast cancer, which is consistent with the previous meta-analysis. However, we noticed that the previous review found depression might be a risk factor for breast cancer (RR, 2.5; 95%CI, 1.06-5.91) if study population were followed more than 10 years. In our review, this association in subgroup analysis by follow-up more than 10 years was not proved. To our knowledge, the larger size of participants, the stronger evidence of the study. The combined results of our meta-analyses are more credible with relatively narrow confidence intervals. Considering the limited number of the included studiesof the previous meta-analysis, we can not conclude that there is significant association between depression and breast cancer.Experimental animal studies, human studies andclinical evidence suggest that depression may put an influence on the development of breast cancer through several mechanisms, such as impairing immune function, causing an aberrant activity of the hypothalamic-pituitary- adrenal axis and inhibiting DNA repairmechanisms (Kiecolt-Glaser et al., 2002; Reiche et al., 2005; Soygur et al., 2007). However, epidemiological research evidences did not indicate the presence of sucha relationship between depression and breast cancer. The and epidemiological studies may be explained by two reasons. On the one hand, the strength of experimental evidence may be compromised due to species differences, inconsistent of laboratory conditions and the measurement of biomarker. Some experiments could not be replicated by different investigators. On the other hand, epidemiological studies may have some methodological flaws, such as insufficient follow-up duration, different definitions and measurement of exposure, the size of sample and so on. Overall, evidence supporting that depression increases the risk of breast cancer are insufficient.There are two strengths in our meta-analysis. Firstly,all studies in the present analyses were cohort studies,which minimized the selection and recall bias. Although our review is an updated meta-analysis, it provides robust and credible conclusion for the association between depression and breast cancer. Secondly, most of studies included in this meta-analysis had average follow-up times more than 10 years. Sufficiently long follow-up duration is necessary because most cancers have a latent period of a few years or even decades (Spratt et al., 1996; Friberget al., 1997). Thus, our results based on long follow-up duration studies could indicate that the depression might not increase the risk of breast cancer.Limitations: A few limitations of our meta-analysis should be acknowledged. Firstly, depression was only measured on the basis of a single baseline measure, which was clearly not identical to depression diagnosis. During the follow-up duration, the exposure intensity of subjects would change. Penninx et al (1998) (Penninx et al., 1998) proved that repeated assessment of depressive symptoms yielded positive association with later developmentof some cancers, in contrast to single measurements. Therefore, a one-time assessment of depression withno measure of duration weakens the test of hypothesis.Secondly, no less than 8 different scales were used for the measurement of depression in the 11 original studies. It may add to the multiple conceptual problems concerned with the definition of depression (Buntinx et al., 2004), which could increase the heterogeneity in our metaanalyses. In conclusion, available epidemiological evidencesare insufficient to support association between depression and the development of breast cancer. Given the high prevalence and morbidity of depression and breast cancer, the results of this meta-analysis not only can act as the clue of the etiology, but can provide the evidence to women who believed that depression could increase the risk of breast cancer.Acknowledgements。
meta分析论文写作
meta分析论⽂写作 meta分析对具备特定条件的、同课题的诸多研究结果进⾏综合的⼀类统计⽅法。
下⾯是⼩编为⼤家精⼼推荐的meta分析论⽂写作，希望能够对您有所帮助。
meta分析论⽂写作篇⼀ 中药治疗痛风临床疗效Meta分析 (1.南京医科⼤学第⼀附属医院，江苏南京 210029;2.南京中医药⼤学，江苏南京 210046) 摘要：⽬的：系统评价中药治疗痛风的疗效及安全性。
⽅法：全⾯检索已发表的中医药治疗痛风临床试验的相关⽂献，采⽤RevMan4.2软件对其统计分析。
结果：共有25个随机对照试验1750例病⼈满⾜纳⼊标准。
与西药组相⽐，中药治疗痛风有效率的合并检验分析结果为：Z4.69，P0.00001，合并后的RR值为1.08，95%的可信区间为(1.05，1.11);药物不良反应发⽣率的⽐较综合检验结果为:Z11.37，P0.00001，合并后的OR值为0.05，95%的可信区间为(0.03，0.08)。
结论：中药治疗痛风有较好的疗效和较低的不良反应发⽣率。
关键词：中药;痛风;系统评价;Meta分析 中图分类号：R259.897⽂献标识码：A⽂章编号：1673-7717(2011)03-0666-05 Meta-Analysis on Clinical Therapeutic Effects of TCM on Gout YUAN Hong-yu 1，HE Miao2，OU Ning1 (1.The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029，Jiangsu,China; 2.Nanjing University of Chinese Medicine,Nanjing 210046,Jiangsu,China) Abstract:Objective:To assess the efficacy and safety of TCM for gout systematically. Methods The pertinent literatures of TCM for gout searched by electronic databases were analyzed with RevMan4.2. Results:Twenty-five randomized controlled trials with 1750 patients were met the inclusion pared with chemical medicine, the combined statistical result of efficiency rate of TCM was Z4.69,P0.00001,RR1.08 (95% CI, 1.05 to 1.11), that of ADR was Z11.37,P0.00001,OR0.05,(95% CI,0.03 to 0.08).Conclucion:There were better therapeutic effect and lower incidence of ADR of TCM on gout. Key words:TCM;Gout;System evaluation;Meta analysis 痛风是嘌呤代谢紊乱或尿酸排泄减少所引起的⼀组疾病，临床表现为特征性反复发作的急性关节炎，痛风⽯沉积，痛风性慢性关节炎和关节畸形，常累及肾脏，引起慢性间质性肾炎和尿酸性尿路结⽯形成，严重时可出现关节毁损致残，肾功能不全。
BMJ的一篇meta分析范文分享
BMJ的⼀篇meta分析范⽂分享随着科学研究的发展，医学研究数量急剧增加，医⽣和研究者全⾯了解医学信息受到时间和资源所限，所以产⽣了对原始⽂献的结果进⾏综合分析的需求。
Meta分析是对具有相同研究⽬的的多个独⽴研究结果进⾏系统分析的⼀种研究⽅法。
该⽅法源于1920年Fisher“合并P值”的思想，1976年⼼理学家Glass进⼀步将其发展为“合并统计量”，并⾸次将这类分析命名为“meta-analysis”。
meta分析是对⽂献资料进⾏系统评价的基本统计⽅法，有助于医⽣和研究者快速把握已有的医学研究信息，以便对已有的研究结论进⾏恰当理解和应⽤。
今天⼩编带⼤家看看⼀篇可谓范⽂的meta分析。
这是⼀篇2014年发表在BMJ上的meta分析：1、作者⾸先提出临床问题：在健康⽆症状感染⼈群中进⾏Hp根除治疗，是否可预防胃癌发⽣。
⽆疑，这是⼀个医学界⾮常关注的、有意义的问题。
2、制定⽂献的纳⼊、排除标准，作者设定了详细的⽂献纳⼊、排除标准：3、检索⽂献：规定检索范围（Medline(1946 to December 2013), Embase(1947 to December 2013), and the Cochranecentral register of controlled trials），并对会议论⽂集进⾏⼿⼯检索，选择可能符合条件的研究，联系这些只发表了会议摘要的研究者，要求他们提供完整的数据集或论⽂。
检索策略作者以单独的附件形式列出，共56条：……4、筛选⽂献：作者列出根据纳⼊、排除标准进⾏⽂献评价的流程图如下：5、提取数据：设定信息提取表，列出所要提取的信息，并进⾏敏感性分析。
6、对纳⼊的研究进⾏偏倚风险评价：这篇meta分析纳⼊的都是RCT研究，偏倚风险评价由两名研究者根据Cochrane⼿册独⽴完成，分歧通过讨论解决。
涉及随机化、随机⽅案隐藏、盲法实施、失访率等。
7、数据合并、统计学分析：作者应⽤随机效应模型以得到更保守和稳健的估计，并进⾏多个亚组分析。
meta分析范文
meta分析范文Meta-analysis is a statistical technique used to combine the results of multiple studies in order to provide a more comprehensive and accurate understanding of a particular research question. It allows researchers to synthesize the findings of various studies and draw more reliable conclusions than any single study could provide.In this paper, we will discuss the process of conducting a meta-analysis, its advantages, and its potentiallimitations.The first step in conducting a meta-analysis is todefine the research question and establish inclusion and exclusion criteria for the studies to be included in the analysis. This involves identifying the relevant literature, searching for studies that meet the criteria, and then selecting the studies that will be included in the analysis. Once the studies have been selected, the next step is to extract the relevant data from each study and convert itinto a common format that can be used for analysis.After the data has been extracted, the next step is to analyze the data using statistical techniques. Thistypically involves calculating effect sizes, which measure the strength of the relationship between variables, andthen combining the effect sizes from the individual studies to produce an overall estimate of the effect. This estimate can then be used to draw conclusions about the research question and to assess the overall strength of the evidence.One of the key advantages of meta-analysis is that it allows researchers to synthesize the findings of multiple studies, which can provide a more comprehensive andreliable understanding of a particular research question.By combining the results of multiple studies, researchers can increase the statistical power of their analysis and draw more reliable conclusions than any single study could provide. This can be particularly useful when individual studies have produced conflicting results, as meta-analysis can help to identify the sources of the discrepancies and provide a more accurate estimate of the true effect.Another advantage of meta-analysis is that it can help to identify patterns and trends that may not be apparent in individual studies. By combining the results of multiple studies, researchers can identify consistent findings and explore potential sources of variation across studies. This can help to generate new hypotheses and guide future research in the field.Despite its many advantages, meta-analysis also has some potential limitations that should be considered. One potential limitation is publication bias, which occurs when studies with positive results are more likely to be published than studies with negative results. This can lead to an overestimation of the true effect, as the published literature may not accurately reflect the full range of findings on a particular research question. To address this limitation, researchers can use statistical techniques such as funnel plots to assess the presence of publication bias and adjust their estimates accordingly.Another potential limitation of meta-analysis is the risk of including low-quality studies, which can bias theoverall estimate of the effect. To address this limitation, researchers can use inclusion criteria to select only high-quality studies for inclusion in the analysis and conduct sensitivity analyses to assess the robustness of their findings.In conclusion, meta-analysis is a powerful tool that can provide a more comprehensive and reliable understanding of a particular research question by synthesizing the findings of multiple studies. By combining the results of individual studies, researchers can increase thestatistical power of their analysis, identify patterns and trends, and draw more reliable conclusions than any single study could provide. However, it is important to consider the potential limitations of meta-analysis and take steps to address them in order to ensure the reliability and validity of the findings.。
《2024年Meta分析系列之五_贝叶斯Meta分析与WinBUGS软件》范文
《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软件的高质量范文一、引言随着现代统计学的发展，Meta分析已经成为评估大量独立研究结果和合成信息的重要方法。
而贝叶斯Meta分析则因其考虑了参数的不确定性以及潜在信息（例如：先前研究的结果、数据先验分布等）的融入，受到了广泛的关注。
本篇将探讨贝叶斯Meta 分析的基本原理、步骤，以及使用WinBUGS软件进行实际操作。
二、贝叶斯Meta分析概述贝叶斯Meta分析是基于贝叶斯定理，利用已知信息（先验信息）和样本数据（后验信息）共同决定未知参数的概率分布。
它考虑了每个研究的权重和效应的随机性，能更好地解决不同研究中异质性的问题。
与传统的频数方法相比，贝叶斯方法可以更好地利用和整合已有的信息和不确定性，提供了更全面的统计推断。
三、贝叶斯Meta分析的步骤1. 确定研究问题和目标：明确Meta分析的目的和问题，选择合适的数据库和文献来源。
2. 文献筛选与数据提取：根据研究问题和目标，筛选相关文献并提取所需数据。
3. 构建模型：根据数据的性质和特点，选择合适的模型（如随机效应模型或固定效应模型）。
4. 参数设置：确定效应参数的先验分布和其他必要参数。
5. 运行分析和结果解释：利用贝叶斯软件进行参数估计、后验推断以及预测等分析。
四、WinBUGS软件的使用WinBUGS软件是用于贝叶斯分析的专业工具，具有良好的图形界面和广泛的应用领域。
使用WinBUGS软件进行贝叶斯Meta分析的具体步骤如下：1. 安装和启动WinBUGS软件。
2. 导入数据：将提取的数据导入WinBUGS软件中。
3. 构建模型：根据数据特点选择合适的模型，并设置参数的先验分布。
4. 运行模型：设置迭代次数、保存间隔等参数，运行模型进行参数估计和后验推断。
meta分析讨论部分万能模板
meta分析讨论部分万能模板本文主要讨论 meta分析部分万能模板，希望大家阅读后有不同的意见和看法，欢迎在下方留言讨论，欢迎来分享。
首先感谢张教授给我们带来一个非常好实用的学习平台。
虽然张教授也是临床医生，但对于 meta分析这方面的知识和经验，我还是有一些不足之处。
但鉴于我对 meta分析这个领域还不是很了解，所以在此先分享一下本人的理解。
一、临床常见疾病的定义为了对临床常见疾病进行研究，可以从以下角度进行：如疾病名称，诊断方式，临床表现，发病机制，治疗方式，预后，原因，发病机制分析，疗效评价等。
以上问题都可以通过meta分析来解决：疾病名称——患者名称，诊断方式——入院时检查结果，治疗方式——用药疗程——不良反应（严重不良反应）——患者治疗结果（一般不良反应）——治疗效果评价。
其中，诊断方式包括：入院时诊断方式（即入院诊断）——住院诊断——不良反应报告方式（即住院记录反馈）。
二、基本要求根据要求，首先，有两个基本的要求：一是发表的研究必须要被发表；二是研究必须在中国或者世界其他国家的医学期刊发表，不能以国外文献为参照来分析中国或者世界其他国家的患者。
这两个要求是不能太高的，否则就会出现严重的漏报事件，造成研究结果的错误或者无效。
其次，发表的文献不能对受试者采取措施，比如，将受试者带离。
不能对受试者采取控制措施以及药物使用（比如停用强抗或降低激素剂量),不能用任何方式来限制试验中受试者的活动。
最后一点便是对于中国或世界其他国家患者要予以特殊照顾。
特别注意的是研究对象最好是非中国或世界其他国家医院、社区或其他医疗机构或者实验室等有特定能力进行临床试验或是观察研究受试者身体状况等而不能是外国人做的。
三、数据质量由于数据质量不是一件容易的事，所以对于研究质量一定要非常关注，可以多关注文献质量问题。
数据质量主要包括数据来源是否正规，是否在有效期限内，是否存在数据不一致情况，是否存在重复样本量等问题在内。
meta分析范文
作为初学者,怎么学写meta分析？ -可以这样学写 meta 分析的基本步骤（1）明确简洁地提出需要解决的问题。
（2）制定检索策略，全面广泛地收集随机对照试验。
（3）确定纳入和排除标准，剔除不符合要求的文献。
（6）统计学处理。
a.异质性检验（齐性检验）。
.统计合并效应量（加权合并，计算效应尺度及95%的置信区间）并进行统计推断。
c.图示单个试验的结果和合并后的结果。
d.敏感性分析。
e.通过“失安全数”的计算或采用“倒漏斗图”了解潜在的发表偏倚。
（帮助他人，快乐自己，若我的回答能够帮助到你，请选择设为“好评”，谢谢你的支持。
）。
如何写meta分析最好能提供好的网站和书籍,谢谢帮助一篇成功的meta分析论文有哪些良好的基础构建谈谈我在科研文案这条路上（帮很多人写过）总结出来的一点心得经验：一：选题一个好的选题就成功了一半。
选题大小决定了工作量大小，选题的争议性、新颖性、临床实用性决定了题目的价值，也决定了以后文章投稿的难易程度。
二：文献检索检索一般强调查准率与查全率。
两者矛盾，但meta分析要求查全要高，所以检索制定要合适。
既不能让初筛文章太多，工作量太大，也不能遗漏重要文献。
三：数据提取两个平行进行，尽量不进行讨论，等数据提取完后，由第三方确认或讨论解决。
四：数据处理目前用的较多软件：STATA,REVMAN，以及诊断性meta的meta-disc。
五：文章写作严格按照相应的指南与手册进行。
如RCT meta的PRISM，观察性研究的MOOSE 等。
meta分析的概念是什么？meta分析国内翻译为“荟萃分析”，定义是“Thestatisticalanalysisoflargecollectionofanalysisr esultsfromindividualstudiesforth epurposeofintegratingthefindings.”中文翻译：对具备特定条件的、同课题的诸多研究结果进行综合的一类统计方法。
《2024年Meta分析系列之六_间接比较及网状分析》范文
《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的高质量范文一、引言随着临床研究在各领域日益发展，多种治疗方法和手段相继涌现，给医学工作者和临床医生带来了许多困惑。
为解答这些问题，越来越多的学者利用Meta分析的方法，以汇总和分析不同的临床试验数据，以期提供更具信度和说服力的证据。
在Meta分析中，间接比较及网状分析是重要的研究方法之一。
本文将通过一个高质量的范文，详细介绍间接比较及网状分析的步骤、方法和注意事项。
二、研究背景本文以某类慢性疾病的治疗方法为研究对象，通过对已发表的随机对照试验进行Meta分析，探讨不同治疗方法之间的疗效差异。
由于该领域涉及多种药物、手术、非药物治疗等多种方法，直接比较各种治疗方法可能存在困难，因此采用间接比较及网状分析方法进行探讨。
三、方法（一）文献检索根据研究主题，使用专业数据库（如Cochrane图书馆、PubMed等）检索相关文献。
选取关键时间范围、试验类型（如随机对照试验）、语言等关键词进行筛选。
（二）纳入与排除标准明确纳入与排除标准，如试验设计类型、患者类型、治疗方法等。
选择高质量的研究，并排除低质量或存在较大偏倚的研究。
（三）数据提取与整理对符合纳入标准的研究进行数据提取，包括研究设计、试验组和对照组的治疗方法、患者人数、样本量、随访时间、结局指标等。
将数据进行整理，以便进行Meta分析。
（四）间接比较及网状分析利用统计软件进行间接比较及网状分析。
首先建立网状模型，将各种治疗方法及其相关数据连接起来。
然后，采用贝叶斯法或其他相关方法对模型进行计算，以得到不同治疗方法之间的疗效差异和排序概率。
最后，绘制网状图和森林图等图表，直观地展示研究结果。
四、结果通过Meta分析，得到了不同治疗方法之间的疗效差异及其排序概率。
在网状图中，各节点代表不同的治疗方法，节点之间的连线表示治疗方法之间的比较和差异。
【2018-2019】meta论文范例-优秀word范文 (6页)
本文部分内容来自网络整理，本司不为其真实性负责，如有异议或侵权请及时联系，本司将立即删除！== 本文为word格式，下载后可方便编辑和修改！ ==meta论文范例篇一：Meta分析在药物评价中的应用论文Meta分析在药物评价中的应用【摘要】meta分析指用统计学方法对收集的多个研究资料进行分析和概括,以提供量化的平均效果来回答所研究的问题。
meta分析能增加样本含量，减少随机误差所致差异，从而增大检验效能和效应量估计精度；同时探讨多个研究结果之间的异质性，将不一致的研究结果进行定量综合，解决了分歧问题。
meta 分析用于药物评价，可以正确认识药物的疗效与风险，为药物的临床应用提供科学依据。
【关键词】meta分析；系统综述；药物评价【中图分类号】r311;r96 【文献标识码】a 【文章编号】1004-7484（201X）10-0016-01meta分析是文献的量化综述，是指用适当的统计学方法对收集的多个研究资料进行分析和概括,以提供量化的平均效果来回答所研究的问题。
meta分析的基本思想源于20世纪30年代的“合并p值”思想， beecher于1955年首次提出初步的概念[1]，1976年心理学家glass进一步按照其思想发展为“合并统计量”，称之为meta分析,并将其定义为：对若干独立的统计结果进行综合、分析的统计方法[2-3]。
与传统的描述性综述相比，设计严密的meta分析能对证据进行更客观的评价,对效应指标进行更准确、客观的评估，并能解释不同研究结果之间的异质性。
其分析符合人们对客观规律的认识过程，是与循证医学思想完全一致的，是医学的巨大进步。
篇二：一篇成功的meta分析论文有哪些良好的基础构建一篇成功的meta分析论文有哪些良好的基础构建医刊汇 5018跟您说Meta是一个科学的临床研究活动，指全面收集所有相关研究并逐个进行严格评价和分析，再用定量合成的方法对资料进行统计学处理得出综合结论的整个过程；meta分析自二十世纪八十年代中期开始被引入到对随机对照和观察性临床研究的归纳评价中，其在医学应用中的主要目有四个：一、提取多个临床研究的数据，从而将单独临床研究中有限的病例数整合为较大的样本量，提高统计效能。
一篇标准meta分析范文中文
一篇标准meta分析范文中文一、标题1 题目：点明主题。
题目一般可定为****Meta分析或****效果评价。
二、摘要2摘要：尽量简明扼要。
包含以下部分：目的、方法、结果、结论。
按照各自需求可适量加上背景、资料来源、纳入标准、研究人群、干预措施、质量评价方法和限制、对主要结果的分析等。
三、关键词3 关键词：为此文章的关键字，能点出文章的主题。
一般为3-4个词，不宜过多。
四、引言4引言：对此文章主题的简要介绍、发展近况、新颖性、有何意义等等。
五、资料与方法5 纳入标准：使用纳入研究的方法学特征（如试验方法，随访时间）和报告特征（如发表年份、语言、发表状态）作为可靠、合理的标准6 信息来源：在检索策略中列出所有的信息来源（如使用的数据库、与研究作者联系获得详细信息）和最后检索日期。
一般英文检索可用Pubmed数据库，中文检索可用我们学校的CNKI数据库及万方数据库等。
7 检索：至少提供一个数据库的完整检索方式，包括对检索的限制，检索词，这个策略是否能被重复使用。
8 研究筛选：表明研究筛选过程，提供检索、纳入标准、质量评价后的纳入研究的数目，每个阶段给出排除理由，最好提供流程图。
9 统计分析方法：所用的分析软件和分析方法，一般有Stata软件或Revman软件。
六、结果10 资料提取：从研究中提取资料，列出所有的文献的研究特征，如发表年份、来源地区、作者、实验数据、随访时间、质量评价等。
该部分用表格的方式给出。
11 单个研究的偏倚：描述用于评价每个研究的偏倚危险的方法（提供是在实施阶段或结局阶段），在数据合成过程中是如何使用这些方法的12 合成方法：描述主要的合成方法（如危险度、均差）13 合成结果：描述数据处理方法和合成的结果，在每个Meta分析中进行异质性检验（I?），不存在异质性则采用固定效应模型，否则用随机效应模型。
14 研究的结果：对于所有呈现的结局（危害、有益）：①每个干预组的简单总结表；②估计效应值和置信区间。
医学mate分析范文
医学mate分析范文一、前言。
大家好！今天咱们来唠唠关于[具体疾病名称]的那些事儿。
你说这病吧，就像个小恶魔，老是折腾患者。
不过呢，现在医学上有好几种治疗方法，可到底哪种最有效呢？这就像在一堆宝藏里找最闪亮的那颗钻石一样，得好好研究研究。
所以呢，咱们就来做个Meta分析，把那些关于这些治疗方法的研究都汇总汇总，看看能不能得出个靠谱的结论。
二、资料与方法。
# （一）资料来源。
咱就像个小侦探一样，在各大医学数据库里搜索相关的研究。
像PubMed、Web of Science、知网这些地方，都被我们翻了个遍。
搜索的关键词就是[疾病名称]和各种相关的治疗方法，比如说[治疗方法1]、[治疗方法2]之类的。
# （二）纳入与排除标准。
这就像是在挑选参赛选手一样。
纳入的研究得是专门针对[具体疾病名称]的，而且得明确提到使用了我们感兴趣的那些治疗方法，还得有一些关键的数据，像治疗后的有效率啊、不良反应这些。
那排除的呢，就是那些数据不全的，或者研究质量特别差的，就像那些没好好准备就来参赛的选手，可不能要。
# （三）数据提取与质量评价。
从那些入选的研究里提取数据可不容易，就像从一堆乱麻里抽出有用的线一样。
我们得仔细地把每个研究里关于患者的基本情况、治疗方法的具体细节、治疗效果这些数据都找出来。
然后呢，还要对这些研究的质量进行评价。
这就好比是给选手打分，看看这个研究设计得合理不合理，有没有什么漏洞。
我们用的是[具体的质量评价工具]来打分的。
三、结果。
# （一）研究的基本情况。
经过一番搜索和筛选，我们总共找到了[X]项研究符合要求。
这些研究来自不同的国家和地区，就像世界各地的小伙伴都来参加这个关于[具体疾病名称]治疗的大讨论一样。
患者的年龄啊、性别啊、病情严重程度这些也都有一定的差异。
# （二）治疗效果的Meta分析。
1. [治疗方法1]咱们先来看[治疗方法1]。
把这些使用[治疗方法1]的研究数据汇总起来分析，发现它的总体有效率大概是[X]%。
丁香园上的一篇meta分析的体会文章【范本模板】
对meta分析的关注也有很长一段时间了，记得当自己刚刚开始读研究生的时候，导师就建议有机会的话，希望能够去做一篇meta 分析来试试。
所以，当时导师就把他手上的有关循证医学的教材都给了我.基本经典的循证教材基本上都拿给我了。
迫于临床工作的繁忙,自己也没有怎么看，也只是在闲暇之时稍微翻阅一下，很多时候都是看了觉得听没有意思的,将的都是理论性的东西，其中举的实例是有点偏少了。
所以，当自己大概看了之后的状态就是:大概在脑海中知道了meta分析是一个什么东西，如果做这个东西,主要包括哪几个方面的内容。
至于说meta分析具体应该怎么操作，就不清楚了，让自己亲自做一篇meta分析那是不可能的。
然而，由于感觉meta分析对文献检索方面要求的比较多，所以，我就开始去关注检索方面的知识，慢慢的自己就逐渐的熟悉了自己专业常用的杂志及常用的学术追踪网站，最重要的是对各大数据库有了较为熟悉的了解.同时，在闲暇之时，自己对文献代理、*＊＊、注入等技术稍微做了一些了解,使自己在文献查阅及文献信息的掌握方面获得了长足的发展，而此时让自己来做一篇meta分析仍然是不可能的事情。
直到今年春节之后，导师再次建议自己作一篇meta分析，但是,考虑到meta分析很关键的是选题的问题，而自己确实没有很理想的适合做meta分析的题目,所以一直不想去做(考虑另外一个原因是不知道怎么去做吧)。
只是后面觉得凡事都是有第一步的，而自己也已经准备了很久了,还是应该去尝试一下.。
..所以，就这样开始了自己的meta之路。
.第一步：选题。
选题是meta分析很关键的一步，也几乎是决定自己文章档次的一步，所以，自己之前一直没有去做，主要原因也是因为没有好的题目.但是，正是因为自己没有好的题目,所以,不愿意真正深入的了解meta,也没有机会真正深入的去了解meta之路的辛酸苦楚。
..当然，也就不会有进一步发展的机会...所以，没有好的题目，如果是新手，比如是研一的学生，时间比较充足，可以先大概找一个题目来练练手，还是会有很多收获的。
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BMJ 的一篇 meta 分析范文分享
随着科学研究的发展，医生和科研工作者越来越需要全
面实时了解医学信息，但往往受到时间和资源的限制，所以
产生了对原始文献的结果进行综合分析的需求，这就催生了
一门专业学科——Meta 分析。
Meta 分析能对同一课题的多
项研究结果的一致性进行评价；提出一些新的研究问题，为
进一步研究指明方向；当受制于某些条件时，如时间或研究
对象的限制， meta 分析不失为一种好的选择；对小样本的临
床实验研究， meta 分析可以统计效能和效应值估计的精确
度。
因此，设计合理、严密的 meta 分析文章能对证据进行更
客观的评价（与传统的描述性的综述相比），对效应指标进
行更准确、客观的评估，并能解释不同研究结果之间的异质
性。
盟主今天就带大家看看一篇可谓范文的 meta 分析。
这是一篇2014 年发表在 BMJ 上的 meta 分析： 1、作者首先提出
临床问题：在健康无症状感染人群中进行 Hp 根除治疗，是否可
预防胃癌发生。
无疑，这是一个医学界非常关注的、有
意义的问题。
2、制定文献的纳入、排除标准，作者设定了
详细的文献纳入、排除标准：
3、检索文献：规定检索范围（Medline(1946 to December 2013), Embase(1947 to December 2013), and the Cochranecentral register of controlled trials ），并对会议论文集进行手工检索，
选择可能符合条件的研究，联系这些只发表了会议摘要的研
究者，要求他们提供完整的数据集或论文。
检索策略作者以
单独的附件形式列出，共56 条： 4、筛选文献：作者列
出根据纳入、排除标准进行文献评价的流程图如下：5、提取数据：设定信息提取表，列出所要提取的信息，并进行敏
感性分析。
6、对纳入的研究进行偏倚风险评价：这篇meta 分析纳入的都是 RCT 研究，偏倚风险评价由两名研究者根据Cochrane 手册独立完成，分歧通过讨论解决。
涉及随机化、随机方案
隐藏、盲法实施、失访率等。
7、数据合并、统计学分析：作者应用随机效应模型以得到
更保守和稳健的估计，并进行多个亚组分析。