Meta analysis, data source

Cochrane Library. The Cochrane Library [44] includes The Cochrane Database of Systematic Reviews, a collection of regularly updated, systematic reviews of the effects of health care. It is maintained by contributors to the Cochrane Collaboration. Cochrane reviews are reviews mainly of randomized controlled trials. To minimize bias, evidence is included or excluded on the basis of explicit quality criteria. Data are often combined statistically, with meta-analysis, to increase the power of the findings of numerous studies, each too small to produce reliable results individually. Database of Abstracts of Reviews of Effectiveness is also included. It consists of critical assessments and structured abstracts of good systematic reviews published elsewhere. The Cochrane Controlled Trials Register with bibliographic information on controlled trials and other sources of information on the science of reviewing research and evidence-based health care are part of the Cochrane Library. It is commercially available on CD-ROM or the Internet. 

While comprehensive research and surveys were delayed, or lacking, no significant effort had been put toward the overview of the existing data of emissions, sources, distribution, exposure, and fate of POPs. Lee and Kim (1999) collected published data on concentrations of EDCs for almost all environmental media and human tissues and performed a meta-analysis to examine the relationship between the sperm quality of South Korean... 

Microbial biosensors have been developed based on key hydrocarbon degradation pathways. These biosensors can be used as tools to examine the regulation of the degradation pathways. In addition to the use of empirically derived data from laboratory studies, a selective meta-analysis of published data from literature sources using catabolic hydrocarbon biosensors was conducted. The aim of this work was to demonstrate that biosensor specific QSARs may be developed to first assess the specificity of degradation pathways and then to assess the possibility of predicting analyte-specific degradation characteristics. 

It is certainly possible to pool the data from several identical toxicological studies. One approach to this is meta-analysis, considered in detail later in this chapter. For example, if an acute inhalation study was performed where only three treatment group animals survived to the point at which a critical measure (such as analysis of blood samples) was taken, there would not be enough data to perform a meaningful statistical analysis. In such a case, the protocol could be repeated with new control and treatment group animals from the same source. At the end, after assurances that the two sets of data are comparable, the data from survivors of the second study could be combined (pooled) with those from the first. 

It may be possible to undertake a meta-analysis if data are available from a number of similar studies (i.e., asking the same question in the same type of patients and in the same or similar clinical settings). Meta-analyses can explore sources of variability in the results of clinical studies, increase confidence in the data and conclusions, and signal when no further studies are necessary. For guidelines on conduct of meta-analyses of RCTs, see the Quality of Reporting of Meta-analyses (QUORUM) statement at http //www.consort-statement.org/QUOROM.pdf (accessed April 22, 2004). 

Meta-Analysis. Meta-analyses (or pooled analyses) synthesize and compare studies that investigate similar health effects and risk factors. These analyses examine sources of heterogeneity and may clarify relationships between exposures and health effects. Inclusion and exclusion criteria and data analysis methods must be transparently described. Meta-analyses are not helpful when the relationship between exposure and disease is obvious, there are few studies, or the existing studies suffer from severe methodological faults (Blair etal. 1995). Dickersin... 

It may be necessary to ask patients directly for information (e.g., number and length of time of home visits by health professionals). Missing data can be estimated using meta-analysis to combine results from other studies. Alternatively assumptions can be based on expert opinion and then tested using sensitivity analysis. If data is collected from different sources then it will be important to use simulation models to combine the data and take account of the variation. 

Meta-analysis is a statistical technique that compares the effectiveness or safety of two treatments by incorporating the findings from several independent studies (DerSimonian and Laird, 1986). Network meta-analysis (NMA), also sometimes referred to as multiple (or mixed) treatment comparisons (MTCs), is the extension of the traditional meta-analysis of two treatments to simultaneous incorporation of multiple treatments, where in most cases none of the studies compared all the treatments at one time. The goal of NMA is to address the comparative effectiveness or safety of interventions while accoimting for all sources of data (Hoaglin et al., 2011 Jansen et al., 2011). 

The protocol should discuss what information was available prior to designing the meta-analysis and what information motivated the research objectives of the meta-analysis. The reporting should clearly state which trials were conducted and which trial results were known by study investigators prior to the design of the meta-analysis. The protocol should state the trial and patient inclusion, including discussion on any possible publication bias. The protocol should state the sources of the trial and patient data. 

Systematic errors affect the accuracy but not the precision of the result. They are usually errors in calibration or observation where the same incorrect protocol is applied to all measurements. They displace all measurements from the true value by the same amount so they cannot be detected by a statistical analysis of only one data set. However, systematic error can be detected and reduced by comparing data sets from several different sources using meta-analysis and systematic review (Rimstidt et ah, 2012). 

Evidence synthesis is a term used for synthesis of results from diverse sources and covers a wide range of analysis approaches (Sutton and Abrams, 2001). Bayesian Evidence Synthesis (here denoted as BES) is a statistical framework for exphcitly modeling several related and connected sources of data, in which uncertainty in model parameters are incorporated (Jackson et al., 2013). BES can be seen as a complex meta-analysis (Sutton and Abrams, 2001), where complex means to consider multiple effects from an intervention. Classical meta-analyses are usually based on studies that directly have observed the effect of an intervention. A broader view on meta-analyses allows for studies on effects on a lower level which are combined with quantitative modelling to assess the effect of an intervention on a higher level. In this view, a risk assessment can be seen as a meta-analysis (Linkov et al., 2009). Opening up for a quantitative assessment (or complex computer) model to measure effects, makes it possible to synthesize evidence for effects which are difficult, if at all, to empirically observe. In the PVA example, there is for example no possibility of... 

The possibility to include judgments of quality is a great advantage with the principles of BES. This is possible since a BES is designed as a framework to combine multiple source of evidence for meta-analysis. Also, new kind of sensitivity analysis can be made. Spiegelhalter and Best (2003) consider the influence of expert judged qualities in three data sources through the observation process models. 

Octylcyanoacrylate is a medical grade topical tissue adhesive that has been approved for closing surgical incisions and traumatic lacerations. We reviewed animal and human studies that evaluated its use fora variety of surgical indications and specialties. We also performed a meta-analysis of all clinical trials using octylcyanoacrylate. Data sources ... 

Isoleucine has often been considered to be among the next-limiting amino acids for growth in pigs, after Lys, Met, Met+Cys, Thr and Trp. However, there are conflicting reports concerning the requirement for He. Differences in requirement estimates may be due to differences in experimental conditions. The initial two steps of the catabohsm of the branched-chain amino acids (BCAA i.e. He, Val, and Leu) are catalyzed by enzyme-complexes shared by the three BCAA. An excessive supply of one BCAA may therefore stimulate the catabolism of the other BCAA. The objective of this study is to perform a meta-analysis of hterature data and to identify the sources of variation in the reported He requirement. 

The well-known DENDRAL and META-DENDRAL programs (43) are noted as the major AI success in chemical applications over the past decade. However, advances in analytical technology and computer capabilities have led to new approaches (44-56). Information fusion from selected instrumental tools often is a more productive route than exhaustive data analysis from a single source. Furthermore, combination of chromatographic separation with spectral, thermal, and microchemical analyses can be realistically achieved in many laboratories. Generalizing and documenting this trend using an AI approach seemed appropriate at this time. 

Cross comparisons across toxicogenomic datasets provides new statistical questions and subsequent challenges in data analysis. Meta-analyses may integrate datasets from multiple experimental studies consisting of different models (species, source), platforms (array type), statistical techniques (normalization) and design. The first challenge that needs to be addressed is how to properly make comparisons across datasets. To normalize datasets, better results may be achieved when data is first normalized internally and then externally (88). Secondly, equivalent and current annotation is needed to identify common genes across platforms, models, etc. 

The meta-analysts did not have access to the original source data for any of the trials included in the analysis (i.e., they did not have access to participant-level data). 

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