Open Meta-Analyst软件及其功能简介

Meta分析是生产证据的重要方法，其合并效应可以为临床实践指南提供有效的数据支持，分析过程依赖于性能良好的软件。Open Meta-Analyst软件是近年来新研发出来的一款具备多种Meta分析功能、适用于多种类型Meta分析的免费、非编程软件，操作简便，易于学习与掌握。本文以二分类数据为例，介绍Open Meta-Analyst软件的功能及其实现Meta分析的过程，同时分析该软件的优势与不足。     

贝叶斯混合处理比较及WinBUGS实现

 文中介绍贝叶斯混合处理比较(MTC)方法,它从传统Meta分析发展而来,即从标准的双臂试验Meta分析扩展为同时将一系列多个不同处理因素进行相互分析比较并进行综合的方法。MTC包括直接比较和间接比较,当没有直接比较证据时使用间接比较方法。我们引用一个5种干预、53臂、25个临床试验的实例,依贝叶斯MTC方法建模,运用WinBUGS软件和马尔可夫链模拟(MCMC)算法进行模拟,得出不同干预的治疗效果和优劣排序。贝叶斯MTC为Meta分析提供了一种新的思路。     

自研《基层医院临床药学服务软件》的应用体会

目的：总结自研基层医院临床药学服务软件在示范应用过程中的经验。方法：对自研软件的开发背景、功能特点、示范应用过程的经验进行归纳分析。结果与结论：该软件为药师开展临床药学服务提供了有力的信息化技术支持平台,尤其适合在基层医院示范应用,具有良好的社会和经济效益。     

静脉用药合理性软件的二次开发和应用

目的：设计开发静脉药物浓度及滴速合理性审查软件,提高静脉用药审查的准确性和药师审方效率。方法：通过查询药典、药品说明书和专业文献,收集静脉药物的输液浓度,滴速要求等,对现有审方软件进行二次开发设计,并完善其功能。结果：设计出静脉用药合理性审查软件,该软件能有效弥补现有软件的不足,有效提高审方质量和效率。结论：通过数据汇总及软件设计,开发出适合静脉滴注药物浓度审查及滴速监控等功能的二次审方软件,并能不断维护增加其他不适宜用药类别等。提高药师审方工作效率,同时不断干预减少临床不合理用药数量,促进临床安全合理用药。     

电子邮件的设置及收发方法

电子邮件即E-Mail,用户可根据自己的爱好选择不同的工具软件收发E-Mail,我们这里只给出Outlook Express和NetscapeCommunicator Mailbox中的设置情况,其他软件的设置与之类似。 一、Outlook Express 1、双击Outlook Express图标,打开该软件; 2、在最上面一行的菜单中点选“工具”,再点选“帐号”; 3、出现“Internet帐号;窗口后,点击右     

两种计算机软件在医学Meta分析中的应用

Meta分析已越来越多地应用于临床流行病学领域,本文结合实例介绍了Stata和RevMan两款软件在Meta分析中的应用,和普通方法相比,应用计算机软件进行可更个性化、更高效专业地完成Meta 分析.     

Excel软件在药效评价Meta分析中的应用

目的探讨Excel表格软件在药效评价Meta分析中的可行性。方法通过单元格引用和函数引用的方法,编写Excel表格程序。结果实例分析表明,基于Excel表格的Peto法Meta分析,其异质性检验、综合效应分析结果以及森林图和漏斗图与RevMan 4.2软件是一致的。结论在计数资料Meta分析的固定效应模型中,可以采用Excel软件进行Peto法Meta分析。     

统计分析软件UNISTAT5.0简介

介绍了 UNISTAT Ltd公司开发的视窗型统计分析软件—— UNISTAT 5 .0 ,讨论了软件的特点及其所具有的统计分析功能 ,认为该软件统计分析功能齐全、支持多运行模式、操作界面友好、可操作性强、数据输入输出灵活、与 MS Office具有较强的整合性 ,是一款十分富有特色的综合性统计分析软件包。     

含多黏菌素B抗菌方案治疗多重耐药革兰阴性菌HAP/VAP的贝叶斯网状Meta分析

目的 采用贝叶斯网状Meta分析方法比较含多黏菌素B的抗菌方案与其他抗菌方案治疗多重耐药革兰阴性菌医院获得性肺炎/呼吸机相关性肺炎（HAP/VAP）的临床疗效。方法 计算机检索PubMed、EMbase、The Cochrane Library、Web of Science、CNKI、SinoMed、ChiCTR、WanFang Data和VIP数据库，搜集含多黏菌素B的抗菌方案治疗多重耐药革兰阴性菌HAP/VAP的临床对照研究，检索时限均从建库到2022年10月15日。由2位研究员独立筛选文献、提取资料并评估纳入研究的偏倚风险后，采用Stata 17.0和R 4.1.2软件进行贝叶斯网状Meta分析。结果 共纳入1个随机对照试验（RCT）和10项观察性研究，共1 385例患者，涉及8种治疗方案。贝叶斯网状Meta分析结果表明，在临床有效率方面，8种用药方案组间差异无统计学意义（P>0.05），累积排序概率曲线下面积（SUCRA）显示多黏菌素B静脉滴注联合多黏菌素B雾化吸入方案的临床有效率高于多黏菌素B静脉滴注单用或其联合替加环素等方案；在病死率方面，文中涉及的4种用药方案中，S...     

Graphpad prism 5.0在医学论文卡方检验的应用

<正>国内多数学者的医学论文中的统计分析是用SPSS软件完成的,绘图大多使用Excel,虽说SPSS软件专业、Excel绘图功能强大,但完成统计分析及绘制复杂及专业的统计图表时,对于医生或者研究者来说也有不小的挑战。本文介绍一款更适合科学统计及医学期刊发表的统计软件——Graphpad prism。1 GraphPad Prism简要介绍GraphPad Prism是拥有基本的生物统计功能、曲线拟合和科学作图功能,并能将三者紧密结合的优秀软件。由于该软件对重复性实验易于组织、分析和作图,易于选择合适的统     

A Bayesian Meta-analysis Method for Estimating Risk Difference of Rare Events

Bayesian meta-analysis has been more frequently utilized for synthesizing safety and efficacy information to support landmark decision-making due to its flexibility of incorporating prior information and availability of computing software. However, when the outcome is binary and the events are rare, where event counts can be zero, conventional meta-analysis methods including Bayesian methods may not work well. Several methods have been proposed to tackle this issue but the prior knowledge of event rate was not utilized to increase precision of risk difference estimates. To better estimate risk differences, we propose a new Bayesian method, Beta prior BInomial model for Risk Differences (B-BIRD), which takes into account the prior information of rare events. B-BIRD is illustrated using a real data set of 48 clinical trials about a type 2 diabetes drug. In simulation studies, it performs well in low event rate settings.     

Programming of a flexible computer simulation to visualize pharmacokinetic-pharmacodynamic models

Teaching pharmacokinetic-pharmacodynamic (PK/PD) models can be made more effective using computer simulations. We propose the programming of educational PK or PK/PD computer simulations as an alternative to the use of pre-built simulation software. This approach has the advantage of adaptability to non-standard or complicated PK or PK/PD models. Simplicity of the programming procedure was achieved by selecting the LabVIEW programming environment. An intuitive user interface to visualize the time courses of drug concentrations or effects can be obtained with pre-built elements. The environment uses a wiring analogy that resembles electrical circuit diagrams rather than abstract programming code. The goal of high interactivity of the simulation was attained by allowing the program to run in continuously repeating loops. This makes the program behave flexibly to the user input. The programming is described with the aid of a 2-compartment PK simulation. Examples of more sophisticated simulation programs are also given where the PK/PD simulation shows drug input, concentrations in plasma, and at effect site and the effects themselves as a function of time. A multi-compartmental model of morphine, including metabolite kinetics and effects is also included. The programs are available for download from the World Wide Web at http:// www. klinik.uni-frankfurt.de/zpharm/klin/ PKPDsimulation/content.html. For pharmacokineticists who only program occasionally, there is the possibility of building the computer simulation, together with the flexible interactive simulation algorithm for clinical pharmacological teaching in the field of PK/PD models.     

分子靶向治疗三阴性乳腺癌无进展 生存期的网状Meta分析

目的：网状Meta分析靶向药物治疗三阴性乳腺癌(TNBC)的无进展生存期（PFS）。方法：系统检索PubMed、EMBASE.com、the Cochrane Central Register of Controlled Trials(CENTRAL)、Web of Science(via ISI Web of Knowledge)、BIOSIS Previews(via ISI Web of Knowledge)和Chemical Abstracts(CA)等数据库,追踪三阴性乳腺癌靶向治疗的系统评价或综述的参考文献,纳入靶向药物治疗三阴性乳腺癌的随机对照试验,由2名研究人员独立提取纳入研究的数据,并评价纳入研究的方法学质量,贝叶斯网状Meta分析采用WinBUGS 1.4软件。结果：共纳入13个随机对照试验,包含2307例患者。纳入的研究方法学质量较高。直接比较的Meta分析结果显示：对比单纯化疗,贝伐单抗联合化疗能改善PFS（HR=0.63,95%CI：0.39～0.90）;其他靶向治疗药物联合化疗在改善PFS差异无统计学意义。间接比较的Meta分析结果显示8种干预方案之间在改善患者PFS上差异均无统计学意义。结论：靶向药物联合化疗治疗三阴性乳腺癌具有一定的疗效,然而证据的样本量不足,尚需要更多设计严谨、大样本的随机对照试验深入研究。     

Propagation of Population Pharmacokinetic Information Using a Bayesian Approach: Comparison with Meta-Analysis

We investigated the propagation of population pharmacokinetic information across clinical studies by applying Bayesian techniques. The aim was to summarize the population pharmacokinetic estimates of a study in appropriate statistical distributions in order to use them as Bayesian priors in consequent population pharmacokinetic analyses. Various data sets of simulated and real clinical data were fitted with WinBUGS, with and without informative priors. The posterior estimates of fittings with non-informative priors were used to build parametric informative priors and the whole procedure was carried on in a consecutive manner. The posterior distributions of the fittings with informative priors where compared to those of the meta-analysis fittings of the respective combinations of data sets. Good agreement was found, for the simulated and experimental datasets when the populations were exchangeable, with the posterior distribution from the fittings with the prior to be nearly identical to the ones estimated with meta-analysis. However, when populations were not exchangeble an alternative parametric form for the prior, the natural conjugate prior, had to be used in order to have consistent results. In conclusion, the results of a population pharmacokinetic analysis may be summarized in Bayesian prior distributions that can be used consecutively with other analyses. The procedure is an alternative to meta-analysis and gives comparable results. It has the advantage that it is faster than the meta-analysis, due to the large datasets used with the latter and can be performed when the data included in the prior are not actually available.     

Bayesian Estimation and Testing in Random Effects Meta-Analysis of Rare Binary Adverse Events

Meta-analysis has been widely applied to rare adverse event data because it is very difficult to reliably detect the effect of a treatment on such events in an individual clinical study. However, it is known that standard meta-analysis methods are often biased, especially when the background incidence rate is very low. A recent work by Bhaumik et al. proposed new moment-based approaches under a natural random effects model, to improve estimation and testing of the treatment effect and the between-study heterogeneity parameter. It has been demonstrated that for rare binary events, their methods have superior performance to commonly used meta-analysis methods. However, their comparison does not include any Bayesian methods, although Bayesian approaches are a natural and attractive choice under the random-effects model. In this article, we study a Bayesian hierarchical approach to estimation and testing in meta-analysis of rare binary events using the random effects model in Bhaumik et al. We develop Bayesian estimators of the treatment effect and the heterogeneity parameter, as well as hypothesis testing methods based on Bayesian model selection procedures. We compare them with the existing methods through simulation. A data example is provided to illustrate the Bayesian approach as well.     

Practical Bayesian design and analysis for drug and device clinical trials

Perhaps the most valuable contribution of Bayesian methods to health care evaluation involves study design. Drug and medical device clinical trialists are increasingly confronted with data that feature complex correlation structures, and are costly and difficult to obtain. In such settings, Bayesian trial designs are attractive since they can incorporate historical data or information from published literature, thus saving time and expense and minimizing the number of subjects exposed to an inferior treatment. Bayesian designs can also adapt to unexpected changes in the protocol, and allow the investigator to explore the plausibility of various outcome scenarios before any patients are enrolled in the trial. Recently, the FDA Center for Devices has encouraged hierarchical Bayesian statistical approaches which allow for the incorporation of such valuable historical data into the design and analysis of new device trials. The practical application of these methods has only become feasible in the last decade due to advances in computing via Markov chain Monte Carlo (MCMC) methods, especially as implemented in the popular BUGS software package. In this paper we illustrate Bayesian analysis and sample size calculations using BRugs, a function for calling BUGS from R. We provide illustrations in two applied settings where incorporation of available historical information is crucial, one concerning an AIDS drug trial and the other a comparison of left ventricular assist devices (LVADs).     

A systematic review of systematic reviews of homeopathy

Homeopathy remains one of the most controversial subjects in therapeutics. This article is an attempt to clarify its effectiveness based on recent systematic reviews. Electronic databases were searched for systematic reviews/meta-analysis on the subject. Seventeen articles fulfilled the inclusion/exclusion criteria. Six of them related to re-analyses of one landmark meta-analysis. Collectively they implied that the overall positive result of this meta-analysis is not supported by a critical analysis of the data. Eleven independent systematic reviews were located. Collectively they failed to provide strong evidence in favour of homeopathy. In particular, there was no condition which responds convincingly better to homeopathic treatment than to placebo or other control interventions. Similarly, there was no homeopathic remedy that was demonstrated to yield clinical effects that are convincingly different from placebo. It is concluded that the best clinical evidence for homeopathy available to date does not warrant positive recommendations for its use in clinical practice.     

Practical Bayesian Design and Analysis for Drug and Device Clinical Trials

Perhaps the most valuable contribution of Bayesian methods to health care evaluation involves study design. Drug and medical device clinical trialists are increasingly confronted with data that feature complex correlation structures, and are costly and difficult to obtain. In such settings, Bayesian trial designs are attractive since they can incorporate historical data or information from published literature, thus saving time and expense and minimizing the number of subjects exposed to an inferior treatment. Bayesian designs can also adapt to unexpected changes in the protocol, and allow the investigator to explore the plausibility of various outcome scenarios before any patients are enrolled in the trial. Recently, the FDA Center for Devices has encouraged hierarchical Bayesian statistical approaches which allow for the incorporation of such valuable historical data into the design and analysis of new device trials. The practical application of these methods has only become feasible in the last decade due to advances in computing via Markov chain Monte Carlo (MCMC) methods, especially as implemented in the popular BUGS software package. In this paper we illustrate Bayesian analysis and sample size calculations using BRugs, a function for calling BUGS from R. We provide illustrations in two applied settings where incorporation of available historical information is crucial, one concerning an AIDS drug trial and the other a comparison of left ventricular assist devices (LVADs).     

The Posterior Probability of Passing a Compendial Standard,Part 1: Uniformity of Dosage Units

A compendial standard is a function of data generated by one or more quality testing procedures. Since the data are sampled from an underlying distribution, the probability of passing a compendial test (Pa) can be expressed as a function of the distribution's model parameters. Applying Bayesian methodology to such a function, we show how to compute the posterior distribution of Pa and other quantities of interest. We apply this methodology to the USP<905> compendial standard and illustrate the importance of considering interbatch variance. We show that the operating characteristics of this Bayesian approach depend on the underlying model parameters almost exclusively through the population Pa and use this to develop a simple univariate procedure to determine the number of batches needed for a process qualification. We note some advantages of this Bayesian approach compared to current approaches. To show the simplicity of the approach and encourage its use, example R and WinBUGS code are provided.