学术期刊三种分析方法的网络特征比较——以图书情报学核心期刊为例

【目的】探析共被引分析、耦合分析、内容分析在学术期刊分析中的相关性、相似性和差异性,一方面为期刊研究提供方法依据,另一方面有助于拓展期刊研究的视角。【方法】以17种图书情报学核心期刊为例,利用二次指派程序分析法（Quadratic Assignment Procedure,即QAP法）和社会网络分析法（Socialnetwork analysis,即SNA法）,在Ucinet6.0环境下对三种期刊分析方法进行了相关性分析、网络密度分析、块模型分析和核心-边缘结构分析。【结果】结果显示,三种分析方法在学术期刊分析中有相似之处也存在差异。其中,内容分析与共被引分析的相似度较高、与耦合分析的相似度较低。【结论】内容分析法可作为共被引分析、耦合分析的辅助分析方法,以实现优势互补,提高学术期刊分析的完整性和有效性。     

毒物现场快速检测方法的研究进展

在突发中毒事件处理时,毒物现场快速检测是应急处理的关键技术之一。其方法包括感官检测法、生物试验法(动物毒性试验法、动物检测法、植物检测法)、理化分析法(化学法、便携式仪器法)、免疫学方法。本文概述了各类方法在现场处理中的作用及特点的进展,着重介绍化学法和便携式仪器法,并对其在中毒控制中的作用进行展望。     

Potential barriers to the application of multi-factor portfolio analysis in public hospitals: evidence from a pilot study in the Netherlands

Portfolio analysis is a business management tool that can assist health care managers to develop new organizational strategies. The application of portfolio analysis to US hospital settings has been frequently reported. In Europe however, the application of this technique has received little attention, especially concerning public hospitals. Therefore, this paper examines the peculiarities of portfolio analysis and its applicability to the strategic management of European public hospitals. The analysis is based on a pilot application of a multi-factor portfolio analysis in a Dutch university hospital. The nature of portfolio analysis and the steps in a multi-factor portfolio analysis are reviewed along with the characteristics of the research setting. Based on these data, a multi-factor portfolio model is developed and operationalized. The portfolio model is applied in a pilot investigation to analyze the market attractiveness and hospital strengths with regard to the provision of three orthopedic services: knee surgery, hip surgery, and arthroscopy. The pilot portfolio analysis is discussed to draw conclusions about potential barriers to the overall adoption of portfolio analysis in the management of a public hospital.     

Analysing clinical decision analyses

We present a critical review of aspects of clinical decision analysis which uses an application to screening for familial intracranial aneurysms. The analysis is reported together with methods for assessing decision trees. These methods appear to be powerful checks on the usually rather intuitive way in which decision trees are built. The problem of assessing the uncertainty in the results of a decision analysis is discussed in detail. In practice, sensitivity analysis covers nearly every calculation apart from the standard evaluation of the decision tree. Different forms of sensitivity analysis are distinguished and given appropriate names: influence analysis, threshold analysis, full Bayesian analysis, Bayesian influence analysis, attribute analysis, generalization analysis and scenario analysis. The biostatistical community may well contribute to the much needed methodological improvement in decision analysis and its different forms of sensitivity analysis, especially if prepared to look beyond the standard statistical techniques.     

水中汞分析研究进展

该文综述了近年来水样中痕量汞的分离富集与分析测定方法。痕量的汞经过分离与富集后,可以用多种方法进行检测。各种分析方法各具特色,分光光度法简单经济,易操作;原子荧光法分析定量准确;色谱法主要应用于汞的形态分析;而联用技术则使得多种分析可以一次完成。     

Generating best evidence from qualitative research: the role of data analysis

OBJECTIVE: To outline the importance of the clarity of data analysis in the doing and reporting of interview-based qualitative research. APPROACH: We explore the clear links between data analysis and evidence. We argue that transparency in the data analysis process is integral to determining the evidence that is generated. Data analysis must occur concurrently with data collection and comprises an ongoing process of 'testing the fit' between the data collected and analysis. We discuss four steps in the process of thematic data analysis: immersion, coding, categorising and generation of themes. CONCLUSION: Rigorous and systematic analysis of qualitative data is integral to the production of high-quality research. Studies that give an explicit account of the data analysis process provide insights into how conclusions are reached while studies that explain themes anchored to data and theory produce the strongest evidence.     

Qualitative data analysis: conceptual and practical considerations

Qualitative inquiry requires that collected data is organised in a meaningful way, and this is referred to as data analysis. Through analytic processes, researchers turn what can be voluminous data into understandable and insightful analysis. This paper sets out the different approaches that qualitative researchers can use to make sense of their data including thematic analysis, narrative analysis, discourse analysis and semiotic analysis and discusses the ways that qualitative researchers can analyse their data. I first discuss salient issues in performing qualitative data analysis, and then proceed to provide some suggestions on different methods of data analysis in qualitative research. Finally, I provide some discussion on the use of computer‐assisted data analysis.     

医院综合效益评价分档的比较研究

目的为医院综合效益乃至医学研究中其他指标分档提供较合理的方法。方法综合效益分析采用层次分析法(AHP),评价分档采用均值标准差法与聚类分析法进行比较研究。结果聚类分析法分档结果相对较分散、合理;均值标准差法分档结果不甚理想,且大部分集中在低等。结论聚类分析法在医院管理及各类医学指标分类中值得推广。     

A note on the bias of standard errors when orthogonality of mean and variance parameters is not satisfied in the mixed model for repeated measures analysis

The mixed effect models for repeated measures (MMRM) analysis is sometimes used as a primary analysis in longitudinal randomized clinical trials. The SE for the treatment effect in the MMRM analysis is usually estimated by assuming the orthogonality of the fixed effect and variance-covariance parameters, which is the orthogonality property of a multivariate normal distribution, because of default settings of most standard statistical software. However, this property might be lost when analysis models are misspecified and/or data include missing values with the mechanism of being missing at random. In this study, we investigated the effect of the assumption of the orthogonality property on the estimation of the SE for the MMRM analysis. From simulation and case studies, it was shown that the SE with the assumption of orthogonality property had nonnegligible bias, especially when the analysis models assuming heteroscedasticity between treatment groups were applied. We also introduce the SAS code for the MMRM analysis without assuming the orthogonality property. Assuming the orthogonality property in the MMRM analysis would lead to invalid statistical inference, and it is necessary to be careful when applying the MMRM analysis with most standard software.     

临床试验中多处理组比较期中分析方法研究

目的 :介绍临床试验中多处理组比较期中分析方法。方法 :在多处理组比较分析方法的基础上 ,将两处理组比较成组序贯期中分析方法进一步扩展用于多处理组比较期中分析。结果 :多处理组期中分析包括各组的总检验和处理组间的两两比较。结论 :多处理组期中分析的关键是每次期中分析时界值的确定     

A graphical sensitivity analysis for clinical trials with non-ignorable missing binary outcome

Many clinical trials are analysed using an intention-to-treat (ITT) approach. A full application of the ITT approach is only possible when complete outcome data are available for all randomized subjects. In a recent survey of clinical trial reports including an ITT analysis, complete case analysis (excluding all patients with a missing response) was common. This does not comply with the basic principles of ITT since not all randomized subjects are included in the analysis. Analyses of data with missing values are based on untestable assumptions, and so sensitivity analysis presenting a range of estimates under alternative assumptions about the missing-data mechanism is recommended. For binary outcome, extreme case analysis has been suggested as a simple form of sensitivity analysis, but this is rarely conclusive. A graphical sensitivity analysis is proposed which displays the results of all possible allocations of cases with missing binary outcome. Extension to allow binomial variation in outcome is also considered. The display is based on easily interpretable parameters and allows informal examination of the effects of varying prior beliefs.     

Bias in randomised factorial trials

Factorial trials are an efficient method of assessing multiple treatments in a single trial, saving both time and resources. However, they rely on the assumption of no interaction between treatment arms. Ignoring the possibility of an interaction in the analysis can lead to bias and potentially misleading conclusions. Therefore, it is often recommended that the size of the interaction be assessed during analysis. This approach can be formalised as a two‐stage analysis; if the interaction test is not significant, a factorial analysis (where all patients receiving treatment A are compared with all not receiving A, and similarly for treatment B) is performed. If the interaction is significant, the analysis reverts to that of a four‐arm trial (where each treatment combination is regarded as a separate treatment arm). We show that estimated treatment effects from the two‐stage analysis can be biased, even in the absence of a true interaction. This occurs because the interaction estimate is highly correlated with treatment effect estimates from a four‐arm analysis. Simulations show that bias can be severe (over 100% in some cases), leading to inflated type I error rates. Therefore, the two‐stage analysis should not be used in factorial trials. A preferable approach may be to design multi‐arm trials (i.e. four separate treatment groups) instead. This approach leads to straightforward interpretation of results, is unbiased regardless of the presence of an interaction, and allows investigators to ensure adequate power by basing sample size requirements on a four‐arm analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

血细胞分析技术及其进展研究

在阐述血细胞分析技术发展历史的基础上,详细介绍了湿式分析技术在红细胞、血小板、白细胞以及血红蛋白等参数上的测量原理,归纳出各种测量方法存在的利弊,并对前沿血细胞分析技术做了详细的探讨.最后结合作者自身的工作实际,对干式血细胞分析技术原理及应用前景进行了概述,预测了未来血细胞分析技术的发展趋势及万向.     

预防用疫苗临床试验安全性分析和报告的考虑

预防用疫苗(疫苗)的安全性是临床试验重点关注和评价的内容之一。本文结合近年来疫苗注册试验的审评实践,汇总了安全性分析和报告中的常见问题并相应提出系列思考和建议,包括探讨了安全性分析中统计学的作用,提出了安全性分析和报告的框架,涉及分析内容、分析指标和分析维度,其中关注了严重程度的分析;强调安全性风险信号应主动分析,鼓励基于循证的要求开展安全性评价;同时就说明书中收载安全性研究信息的基本原则、以及各期试验及不同受试者安全性数据综合考虑的问题进行了探讨,供疫苗注册申请人参考和借鉴,以促进我国疫苗临床研发和评价。     

Meta分析中随机效应模型的Gibbs抽样及其应用

目的 探讨基于分层贝叶斯的随机效应模型meta分析的Gibbs抽样方法及其应用。方法 在分层贝叶斯框架下，推出随机效应模型meta分析参数的完全条件分布。对中国人HBV、HCV及其双重感染与原发性肝癌关系的16个病例－对照研究数据，用二项分布建模，通过拟合三个logistic模型，在WinBUGS软件包上进行Gibbs抽样。结果 由Gibbs抽样得到HBV、HCV及其双重感染与原发性肝癌关系的效应合并值、研究间方差等参数的后验均数和95％CI。结论 Gibbs抽样不但能灵活地构造模型对复杂问题进行meta分析，而且当纳入研究中数据极端值较多，经典方法不再适应时，用Gibbs抽样仍然可以方便有效地得到参数的后验分布。     

专利信息分析方法及其在微流体侦检技术中应用研究

目的：应用专利信息分析方法对侦检主流技术——微流体技术进行探索性分析,旨在了解当前微流体技术现状和核心技术。方法：专利信息分析方法。结果：微流体技术还处于技术发展期,专利转化为科技生产力的程度不高。结论：专利信息分析方法可用于相关技术的分析,对微流体侦检技术的分析结果可信、技术可行。     

建立健全经营分析制度 提高医疗设备管理水平

通过对医疗设备收支趋势、设备成本结构、设备医疗效益进行分析,将经营分析与医疗分析、全面分析与重点分析、定期分析与专项分析、部门分析与科室分析、目标任务与完成情况进行结合,有效地指导医疗设备经济运行方式,以期全面掌握医疗设备经济运行情况、运行规律,不断提高大型医疗设备管理水平。     

Combining multiple imputation and meta‐analysis with individual participant data

Multiple imputation is a strategy for the analysis of incomplete data such that the impact of the missingness on the power and bias of estimates is mitigated. When data from multiple studies are collated, we can propose both within‐study and multilevel imputation models to impute missing data on covariates. It is not clear how to choose between imputation models or how to combine imputation and inverse‐variance weighted meta‐analysis methods. This is especially important as often different studies measure data on different variables, meaning that we may need to impute data on a variable which is systematically missing in a particular study. In this paper, we consider a simulation analysis of sporadically missing data in a single covariate with a linear analysis model and discuss how the results would be applicable to the case of systematically missing data. We find in this context that ensuring the congeniality of the imputation and analysis models is important to give correct standard errors and confidence intervals. For example, if the analysis model allows between‐study heterogeneity of a parameter, then we should incorporate this heterogeneity into the imputation model to maintain the congeniality of the two models. In an inverse‐variance weighted meta‐analysis, we should impute missing data and apply Rubin's rules at the study level prior to meta‐analysis, rather than meta‐analyzing each of the multiple imputations and then combining the meta‐analysis estimates using Rubin's rules. We illustrate the results using data from the Emerging Risk Factors Collaboration. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.     

Application of the missing-indicator method in matched case-control studies with incomplete data

A common practice in matched case-control studies with incomplete data is to perform two analyses in parallel: a matched analysis of the complete pairs and an unmatched analysis of all subjects carried out after breaking the matching in the complete pairs. The missing-indicator method, which has the advantage of making use of the data in the incomplete pairs while still preserving the matching in the complete pairs, is recommended as an alternative method of analysis. It is shown here that its estimate of the odds ratio is a compromise between the odds ratios estimated by a matched analysis of the complete pairs and an unmatched analysis of the incomplete pairs. The method is illustrated using data from a matched case-control study of the risk of childhood leukemia from exposure to residential electric and magnetic fields.     

Are factor analytical techniques used appropriately in the validation of health status questionnaires? A systematic review on the quality of factor analysis of the SF-36

Factor analysis is widely used to evaluate whether questionnaire items can be grouped into clusters representing different dimensions of the construct under study. This review focuses on the appropriate use of factor analysis. The Medical Outcomes Study Short Form-36 (SF-36) is used as an example. Articles were systematically searched and assessed according to a number of criteria for appropriate use and reporting. Twenty-eight studies were identified: exploratory factor analysis was performed in 22 studies, confirmatory factor analysis was performed in five studies and in one study both were performed. Substantial shortcomings were found in the reporting and justification of the methods applied. In 15 of the 23 studies in which exploratory factor analysis was performed, confirmatory factor analysis would have been more appropriate. Cross-validation was rarely performed. Presentation of the results and conclusions was often incomplete. Some of our results are specific for the SF-36, but the finding that both the application and the reporting of factor analysis leaves much room for improvement probably applies to other health status questionnaires as well. Optimal reporting and justification of methods is crucial for correct interpretation of the results and verification of the conclusions. Our list of criteria may be useful for journal editors, reviewers and researchers who have to assess publications in which factor analysis is applied.