scholarly journals META-ANALYSIS OF MORPHINE PHARMACOKINETICS IN NEONATES USING RAW DATA: 419

1996 ◽  
Vol 39 ◽  
pp. 72-72
Author(s):  
Rama Bhat ◽  
Gopal Chari
Keyword(s):  
Meta Analysis ◽  
Raw Data

scholarly journals Reproducible and Transparent Research Practices in Published Neurology Research

2019 ◽  
Author(s):  
Shelby Rauh ◽  
Trevor Torgerson ◽  
Austin L. Johnson ◽  
Jonathan Pollard ◽  
Daniel Tritz ◽  
...  
Keyword(s):  
Systematic Review ◽  
Conflict Of Interest ◽  
Data Extraction ◽  
Meta Analysis ◽  
Research Practices ◽  
Raw Data ◽  
Replication Studies ◽  
Study Materials ◽  
Funding Sources ◽  
Pubmed Search

AbstractBackgroundThe objective of this study was to evaluate the nature and extent of reproducible and transparent research practices in neurology research.MethodsThe NLM catalog was used to identify MEDLINE-indexed neurology journals. A PubMed search of these journals was conducted to retrieve publications over a 5-year period from 2014 to 2018. A random sample of publications was extracted. Two authors conducted data extraction in a blinded, duplicate fashion using a pilot-tested Google form. This form prompted data extractors to determine whether publications provided access to items such as study materials, raw data, analysis scripts, and protocols. In addition, we determined if the publication was included in a replication study or systematic review, was preregistered, had a conflict of interest declaration, specified funding sources, and was open access.ResultsOur search identified 223,932 publications meeting the inclusion criteria, from which 300 were randomly sampled. Only 290 articles were accessible, yielding 202 publications with empirical data for analysis. Our results indicate that 8.99% provided access to materials, 9.41% provided access to raw data, 0.50% provided access to the analysis scripts, 0.99% linked the protocol, and 3.47% were preregistered. A third of sampled publications lacked funding or conflict of interest statements. No publications from our sample were included in replication studies, but a fifth were cited in a systematic review or meta-analysis.ConclusionsCurrent research in the field of neurology does not consistently provide information needed for reproducibility. The implications of poor research reporting can both affect patient care and increase research waste. Collaborative intervention by authors, peer reviewers, journals, and funding sources is needed to mitigate this problem.

scholarly journals Deep Meta Tool: GUI tool to obtain Mean and Standard Deviation (SD) from Median and Interquartile range (IQR)

2021 ◽  
Author(s):  
Deepanshu Sharma ◽  
Surya Priya Ulaganathan ◽  
Vinay Sharma ◽  
Sakshi Piplani ◽  
Ravi Ranjan Kumar Niraj
Keyword(s):  
Standard Deviation ◽  
Input Data ◽  
Meta Analysis ◽  
Statistical Procedure ◽  
Interquartile Range ◽  
Raw Data ◽  
Sample Data

Abstract Background and objectivesMeta-analysis is a statistical procedure which enables the researcher to integrate the results of various studies that were conducted for the same purpose. However, more often than not, researchers find themselves in a position unable to proceed further due to the complexity of the mathematics involved and unavailability of raw data. To alleviate the said difficulty, we are presenting a tool that will enable researchers to process raw data.MethodsThe GUI tool is written in python. The tool offers an automated conversion and obtainment of mean and standard deviation (SD) from median and interquartile range, utilizing the methods offered by Hozo et al. 2005 and Bland 2015.ResultsThe tool is tested on some sample data and validation is performed for Bland method on the data provided in the Bland method publication (14).ConclusionsThe provided tool is an easy alternative for the preparation of input data required for clinical meta-analysis in the required format.

Meta-Analysis and Meta-Review of Thyroid Cancer Gene Expression Profiling Studies Identifies Important Diagnostic Biomarkers

2006 ◽  
Vol 24 (31) ◽  
pp. 5043-5051 ◽  
Author(s):  
Obi L. Griffith ◽  
Adrienne Melck ◽  
Steven J.M. Jones ◽  
Sam M. Wiseman
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Expression Profiling ◽  
Meta Analysis ◽  
Thyroid Tumors ◽  
Raw Data ◽  
Diagnostic Biomarkers ◽  
Meta Review ◽  
Average Fold Change ◽  
Clinically Significant

Purpose An estimated 4% to 7% of the population will develop a clinically significant thyroid nodule during their lifetime. In many cases, preoperative diagnoses by needle biopsy are inconclusive. Thus, there is a clear need for improved diagnostic tests to distinguish malignant from benign thyroid tumors. The recent development of high-throughput molecular analytic techniques should allow the rapid evaluation of new diagnostic markers. However, researchers are faced with an overwhelming number of potential markers from numerous thyroid cancer expression profiling studies. Materials and Methods To address this challenge, we have carried out a comprehensive meta-review of thyroid cancer biomarkers from 21 published studies. A gene ranking system that considers the number of comparisons in agreement, total number of samples, average fold-change and direction of change was devised. Results We have observed that genes are consistently reported by multiple studies at a highly significant rate (P < .05). Comparison with a meta-analysis of studies reprocessed from raw data showed strong concordance with our method. Conclusion Our approach represents a useful method for identifying consistent gene expression markers when raw data are unavailable. A review of the top 12 candidates revealed well known thyroid cancer markers such as MET, TFF3, SERPINA1, TIMP1, FN1, and TPO as well as relatively novel or uncharacterized genes such as TGFA, QPCT, CRABP1, FCGBP, EPS8 and PROS1. These candidates should help to develop a panel of markers with sufficient sensitivity and specificity for the diagnosis of thyroid tumors in a clinical setting.

scholarly journals Forensic Microbiome Database: A Tool for Forensic Geolocation Meta-Analysis Using Publicly Available 16S rRNA Microbiome Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
Harinder Singh ◽  
Thomas Clarke ◽  
Lauren Brinkac ◽  
Chris Greco ◽  
Karen E. Nelson
Keyword(s):  
16S Rrna ◽  
Personal Information ◽  
Meta Analysis ◽  
Human Microbiome ◽  
Data Set ◽  
Scientific Communities ◽  
Raw Data ◽  
Analysis Techniques ◽  
Dynamic Graphics ◽  
Downstream Analysis

The human microbiome has been proposed as a tool to investigate different forensic questions, including for the identification of multiple personal information. However, the fragmented state of the publicly available data has retarded the development of analysis techniques and, therefore, the implementation of microbiomes as a forensic tool. To address this, we introduce the forensic microbiome database (FMD), which is a collection of 16S rRNA data and associated metadata generated from publicly available data. The raw data was further normalized and processed using a pipeline to create a standardized data set for downstream analysis. We present a website allowing for the exploration of geolocation signals in the FMD. The website allows users to investigate the taxonomic differences between microbiomes harvested from different locations and to predict the geolocation of their data based on the FMD sequences. All the results are presented in dynamic graphics to allow for a rapid and intuitive investigation of the taxonomic distributions underpinning the geolocation signals and prediction between locations. Apart from the forensic aspect, the database also allows exploration and comparison of microbiome samples from different geolocation and between different body sites. The goal of the FMD is to provide the scientific and non-scientific communities with data and tools to explore the possibilities of microbiomes to answer forensic questions and serve as a model for any future such databases.1

scholarly journals Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Joost D. J. Plate ◽  
Rutger R. van de Leur ◽  
Luke P. H. Leenen ◽  
Falco Hietbrink ◽  
Linda M. Peelen ◽  
...  
Keyword(s):  
Systematic Review ◽  
Critical Care ◽  
Care Setting ◽  
Prediction Models ◽  
Meta Analysis ◽  
Repeated Measurements ◽  
Critical Care Setting ◽  
Annual Number ◽  
Observation Window ◽  
Raw Data

Abstract Background The incorporation of repeated measurements into multivariable prediction research may greatly enhance predictive performance. However, the methodological possibilities vary widely and a structured overview of the possible and utilized approaches lacks. Therefore, we [1] propose a structured framework for these approaches, [2] determine what methods are currently used to incorporate repeated measurements in prediction research in the critical care setting and, where possible, [3] assess the added discriminative value of incorporating repeated measurements. Methods The proposed framework consists of three domains: the observation window (static or dynamic), the processing of the raw data (raw data modelling, feature extraction and reduction) and the type of modelling. A systematic review was performed to identify studies which incorporate repeated measurements to predict (e.g. mortality) in the critical care setting. The within-study difference in c-statistics between models with versus without repeated measurements were obtained and pooled in a meta-analysis. Results From the 2618 studies found, 29 studies incorporated multiple repeated measurements. The annual number of studies with repeated measurements increased from 2.8/year (2000–2005) to 16.0/year (2016–2018). The majority of studies that incorporated repeated measurements for prediction research used a dynamic observation window, and extracted features directly from the data. Differences in c statistics ranged from − 0.048 to 0.217 in favour of models that utilize repeated measurements. Conclusions Repeated measurements are increasingly common to predict events in the critical care domain, but their incorporation is lagging. A framework of possible approaches could aid researchers to optimize future prediction models.

scholarly journals Magnetic Resonance Imaging of the Small Bowel in Crohn’s Disease: A Systematic Review and Meta-Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Osman Ahmed ◽  
David Mario Rodrigues ◽  
Geoffrey C. Nguyen
Keyword(s):  
Systematic Review ◽  
Crohn’S Disease ◽  
Magnetic Resonance ◽  
Small Bowel ◽  
Crohn's Disease ◽  
Meta Analysis ◽  
Pooled Analysis ◽  
Likelihood Ratios ◽  
Raw Data ◽  
Sensitivity Specificity

Introduction. Crohn’s disease is most commonly found in the terminal ileum and colonic region. Magnetic resonance has become a useful modality for assessing small bowel activity. In this study, we performed a systematic review and meta-analysis on the use of MR in detecting small bowel activity as well as extramural complications in Crohn’s patients.Methods. Two independent reviewers sorted through articles until October 2, 2014. We included both studies providing raw data for pooling and studies without raw data. Sensitivity, specificity, likelihood ratios, and 95% confidence intervals were calculated for each study.Results. There were 27 included studies, of which 19 were included in the pooled analysis. Pooled analysis of the 19 studies (1020 patients) with raw data revealed a sensitivity of 0.88 (95% CI 0.86 to 0.91) and specificity was 0.88 (95% CI 0.84 to 0.91). In regard to detecting stenosis, pooled sensitivity was 0.65 (95% CI 0.53 to 0.76) and specificity was 0.93 (95% CI 0.89 to 0.96).Conclusion. MR imaging provides a reliable alternative in detecting small bowel activity in patients with Crohn’s disease. Its advantages include high diagnostic accuracy and no radiation exposure while its disadvantages include high cost and limited availability.

Combining Reliability Coefficients: Possible Application to Meta-Analysis and Reliability Generalization

2003 ◽  
Vol 93 (3) ◽  
pp. 643-647 ◽  
Author(s):  
Richard A. Charter
Keyword(s):  
Meta Analysis ◽  
Coefficient Alpha ◽  
Sample Sizes ◽  
Reliability Generalization ◽  
Raw Data ◽  
Alternate Forms ◽  
Standard Deviations ◽  
Reliability Coefficients

Formulae for combining reliability coefficients from any number of samples are provided. These formulae produce the exact reliability one would compute if one had the raw data from the samples. Needed are the sample means, standard deviations, sample sizes, and reliability coefficients. The formulae work for coefficient alpha, KR-20, retest, alternate-forms, split-half, interrater (intraclass), Gilmer-Feldt, Angoff-Feldt, validity, and other coefficients. They may be particularly useful for meta-analytic and reliability generalization studies.

Don't characterize replications as successes or failures

2018 ◽  
Vol 41 ◽  
Author(s):  
Andrew Gelman
Keyword(s):  
Treatment Effects ◽  
Meta Analysis ◽  
Raw Data ◽  
Continuous Measures

AbstractNo replication is truly direct, and I recommend moving away from the classification of replications as “direct” or “conceptual” to a framework in which we accept that treatment effects vary across conditions. Relatedly, we should stop labeling replications as successes or failures and instead use continuous measures to compare different studies, again using meta-analysis of raw data where possible.

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