scholarly journals GEOGRAPHICAL META-ANALYSIS AND ITS FEATURES

2021 ◽  
pp. 6-21
Author(s):  
Aleksander K. Cherkashin ◽  
Keyword(s):  
Meta Analysis ◽  
Heterogeneous Data ◽  
Seasonal Development ◽  
Economic Systems ◽  
Data Sets ◽  
Individual Values ◽  
Analysis Methodology ◽  
Geographical Environment ◽  
Homogeneous Data ◽  
Scientific Hypotheses

Geographical meta-analysis is a methodology for combining the results of studies of various territorial objects of different types of locations by means of logical, mathematical and statistical analysis to justify and test scientific hypotheses. Meta-analytical generalizations are based on a non-statistical approach of comparative geographical research with a transition from initial heterogeneous data sets to homogeneous data that can be statistically processed. The meta-analysis methodology is developed on a meta-theoretical basis from the standpoint of the system stratification (fibering, bundling) of the earth's reality on the manifold of the geographical environment. Locally, the same qualimetric equations for data integration and generalization describes processes and phenomena, so each situation is reduced to the properties of a typical layer (fiber) and universal equations on the connection of variables. Features of using geographical meta-analysis methods are considered on the examples of the spread of COVID-19 coronavirus diseases across countries, seasonal development of taiga nature, and gradient analysis of the factor influence on the distribution of mountain geosystems of various types (geomes). In order to compress information, we use methods for calculating integral indicators and other means of excluding influence from the environment. The revealed regularities do not depend on individual values of factors and conditions that influence the processes and relationships between the characteristics of the state of natural and socio-economic systems. They represent dependencies in a refined form.

Meta-Analysis: Methodology, Utility, and Limitations

1998 ◽  
Vol 11 (4) ◽  
pp. 239-250 ◽  
Author(s):  
Michael A. Mancano ◽  
Michael F. Bullano
Keyword(s):  
Meta Analysis ◽  
Clinical Trial Data ◽  
Data Sets ◽  
Analysis Methodology ◽  
Large Numbers ◽  
Methodological Problems ◽  
Clinical Questions ◽  
The Many ◽  
Many Sources ◽  
Meta Analyses

Meta-analyses are statistical methods to evaluate large numbers of clinical trial data sets to answer specific clinical questions. Meta-analyses can be performed when large clinical trials are not available or prior to their availability. It is essential for pharmacists to effectively evaluate potential methodological problems and be aware of the current controversies surrounding meta-analysis. This article will focus on the components of a properly performed meta-analysis and the many sources of bias inherent in a literature review of this magnitude. A suggested reading list of in-depth references concerning the many aspects of meta-analysis is provided for the interested reader.

The influence of sample plot size on evaluations with Ellenberg indicator values

Biologia ◽  
2009 ◽  
Vol 64 (6) ◽  
Author(s):  
Zdenka Otýpková
Keyword(s):  
Heterogeneous Data ◽  
Data Sets ◽  
Sample Plot ◽  
Robust Method ◽  
Ellenberg Indicator Values ◽  
Indicator Values ◽  
Indicator Value ◽  
Plot Size ◽  
Wide Range ◽  
Homogeneous Data

AbstractThe effect of plot size was tested on heterogeneous and homogeneous data sets that were obtained by sampling grassland and forest vegetation on plots differing in size. Mean EIV for relevés revealed no differences among data sets from various plot sizes or between homogeneous and heterogeneous data sets. This is probably due to a similar indicator value for species newly occurring in plots with increasing plot size. Using EIV is thus a robust method even for data sets associated with wide range of plot sizes.

scholarly journals Cluster analysis on high dimensional RNA-seq data with applications to cancer research - An evaluation study

2019 ◽  
Author(s):  
Linda Vidman ◽  
David Källberg ◽  
Patrik Rydén
Keyword(s):  
Cluster Analysis ◽  
Sample Size ◽  
Human Cancer ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Relative Distribution ◽  
Limited Effect ◽  
Noticeable Effect ◽  
Cancer Data ◽  
Homogeneous Data

AbstractClustering of gene expression data is widely used to identify novel subtypes of cancer. Plenty of clustering approaches have been proposed, but there is a lack of knowledge regarding their relative merits and how data characteristics influence the performance. We evaluate how cluster analysis choices affect the performance by studying four publicly available human cancer data sets: breast, brain, kidney and stomach cancer. In particular, we focus on how the sample size, distribution of subtypes and sample heterogeneity affect the performance.In general, increasing the sample size had limited effect on the clustering performance, e.g. for the breast cancer data similar performance was obtained forn= 40 as forn= 330. The relative distribution of the subtypes had a noticeable effect on the ability of identifying the disease subtypes and data with heavily skewed distributions turned out to be difficult to cluster. Both the choice of clustering method and selection method affected the ability to identify the subtypes, but the relative performance varied between data sets, making it difficult to rank the approaches. For some data sets, the performance was substantially higher when the clustering was based on data from only one sex compared to data from a mixed population. This suggests that homogeneous data are easier to cluster than heterogeneous data and that clustering males and females individually may be beneficial and increase the chance to detect novel subtypes. It was also observed that the performance often differed substantially between females and males.The number of samples seems to have a limited effect on the performance while the heterogeneity, at least with respect to sex, is important for the performance. Hence, by analyzing the genders separately, the possible loss caused by having fewer samples could be outweighed by the benefit of a more homogeneous data.

Comparing Instructional Approaches in Caregiver-Implemented Intervention: An Interdisciplinary Systematic Review and Meta-Analysis

2021 ◽  
pp. 105381512198980
Author(s):  
Bailey J. Sone ◽  
Jordan Lee ◽  
Megan Y. Roberts
Keyword(s):  
Adult Learning ◽  
Family Involvement ◽  
Meta Analysis ◽  
Future Research ◽  
Instructional Approaches ◽  
Comparative Efficacy ◽  
Robust Variance ◽  
Analysis Methodology ◽  
Intervention Models ◽  
Variance Estimate

Family involvement is a cornerstone of early intervention (EI). Therefore, positive caregiver outcomes are vital, particularly in caregiver-implemented interventions. As such, caregiver instructional approaches should optimize adult learning. This study investigated the comparative efficacy of coaching and traditional caregiver instruction on caregiver outcomes across EI disciplines. A systematic search for articles was conducted using PRISMA guidelines. Meta-analysis methodology was used to analyze caregiver outcomes, and a robust variance estimate model was used to control for within-study effect size correlations. Seven relevant studies were ultimately included in the analysis. A significant, large effect of coaching on caregiver outcomes was observed compared to other models of instruction ( g = 0.745, SE = 0.125, p = .0013). These results support the adoption of a coaching framework to optimize caregiver outcomes in EI. Future research should examine how coaching and traditional instruction can be used in tiered intervention models with a variety of populations.

Race-Related Association between APOE Genotype and Alzheimer’s Disease: A Systematic Review and Meta-Analysis

2021 ◽  
pp. 1-10
Author(s):  
Wei Qin ◽  
Wenwen Li ◽  
Qi Wang ◽  
Min Gong ◽  
Tingting Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Black People ◽  
Late Onset ◽  
Meta Analysis ◽  
Group Analysis ◽  
Apoe Genotype ◽  
Cochrane Library ◽  
Data Sets ◽  
Apoe Genotypes

Background: The global race-dependent association of Alzheimer’s disease (AD) and apolipoprotein E (APOE) genotype is not well understood. Transethnic analysis of APOE could clarify the role of genetics in AD risk across populations. Objective: This study aims to determine how race and APOE genotype affect the risks for AD. Methods: We performed a systematic search of PubMed, Embase, Web of Science, and the Cochrane Library since 1993 to Aug 25, 2020. A total of 10,395 reports were identified, and 133 were eligible for analysis with data on 77,402 participants. Studies contained AD clinical diagnostic and APOE genotype data. Homogeneous data sets were pooled in case-control analyses. Odds ratios and 95% confidence intervals for developing AD were calculated for populations of different races and APOE genotypes. Results: The proportion of APOE genotypes and alleles differed between populations of different races. Results showed that APOE ɛ4 was a risk factor for AD, whereas APOE ɛ2 protected against it. The effects of APOE ɛ4 and ɛ2 on AD risk were distinct in various races, they were substantially attenuated among Black people. Sub-group analysis found a higher frequency of APOE ɛ4/ɛ4 and lower frequency of APOE ɛ3/ɛ3 among early-onset AD than late-onset AD in a combined group and different races. Conclusion: Our meta-analysis suggests that the association of APOE genotypes and AD differ between races. These results enhance our understanding of APOE-related risk for AD across race backgrounds and provide new insights into precision medicine for AD.

A Systematic Review and Meta-Analysis of the Relation Between Engagement and Achievement in Early Childhood Research

2021 ◽  
pp. 027112142110327
Author(s):  
Esther R. Lindström ◽  
Jason C. Chow ◽  
Kathleen N. Zimmerman ◽  
Hongyang Zhao ◽  
Elise Settanni ◽  
...  
Keyword(s):  
Early Childhood ◽  
Academic Engagement ◽  
Literature Search ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Data Sets ◽  
Systematic Literature Search ◽  
Random Effects Models ◽  
Unique Data ◽  
Early Childhood Research

Engagement in early childhood has been linked with later achievement, but the relation between these variables and how they are measured in early childhood requires examination. We estimated the overall association between academic engagement and achievement in children prior to kindergarten entry. Our systematic literature search yielded 13,521 reports for structured eligibility screening; from this pool of studies, we identified 21 unique data sets, with 199 effect sizes for analysis. We coded eligible studies, extracted effect sizes, accounted for effect size dependency, and used random-effects models to synthesize findings. The overall correlation between academic engagement and achievement was r = .24 (range: −.08 to −.71), and moderator analyses did not significantly predict the relation between the two constructs. This study aligns with previous research on this topic and examines issues related to these measures, their constraints, and applications as they pertain to early childhood research.

scholarly journals Comparing global tomography-derived and gravity-based upper mantle density models

2020 ◽  
Vol 221 (3) ◽  
pp. 1542-1554 ◽  
Author(s):  
B C Root
Keyword(s):  
Seismic Tomography ◽  
Upper Mantle ◽  
Seismic Data ◽  
Gravity Data ◽  
Heterogeneous Data ◽  
Clear Correlation ◽  
Data Sets ◽  
Satellite Gravity ◽  
Similar Density ◽  
Similar Peak

SUMMARY Current seismic tomography models show a complex environment underneath the crust, corroborated by high-precision satellite gravity observations. Both data sets are used to independently explore the density structure of the upper mantle. However, combining these two data sets proves to be challenging. The gravity-data has an inherent insensitivity in the radial direction and seismic tomography has a heterogeneous data acquisition, resulting in smoothed tomography models with de-correlation between different models for the mid-to-small wavelength features. Therefore, this study aims to assess and quantify the effect of regularization on a seismic tomography model by exploiting the high lateral sensitivity of gravity data. Seismic tomography models, SL2013sv, SAVANI, SMEAN2 and S40RTS are compared to a gravity-based density model of the upper mantle. In order to obtain similar density solutions compared to the seismic-derived models, the gravity-based model needs to be smoothed with a Gaussian filter. Different smoothening characteristics are observed for the variety of seismic tomography models, relating to the regularization approach in the inversions. Various S40RTS models with similar seismic data but different regularization settings show that the smoothening effect is stronger with increasing regularization. The type of regularization has a dominant effect on the final tomography solution. To reduce the effect of regularization on the tomography models, an enhancement procedure is proposed. This enhancement should be performed within the spectral domain of the actual resolution of the seismic tomography model. The enhanced seismic tomography models show improved spatial correlation with each other and with the gravity-based model. The variation of the density anomalies have similar peak-to-peak magnitudes and clear correlation to geological structures. The resolvement of the spectral misalignment between tomographic models and gravity-based solutions is the first step in the improvement of multidata inversion studies of the upper mantle and benefit from the advantages in both data sets.

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