Editorial Code for Presentation of Statistical Analyses

1994 ◽  
Vol 30 (4) ◽  
pp. 377-380 ◽  
Author(s):  
D. J. Finney ◽  
J. L. Harper
Keyword(s):  
Statistical Methods ◽  
Statistical Analyses

The proliferation of statistical methods and software increases the need for authors to state, unambiguously and informatively, the methods they have used. The reader of any paper should expect to learn precisely what the author has done, and to be able to adopt similar procedures in his or her own work.

scholarly journals Editorial, special issue on “Advances in Robust Statistics”

METRON ◽  
2021 ◽  
Author(s):  
Marco Riani ◽  
Mia Hubert
Keyword(s):  
Statistical Methods ◽  
Robust Statistics ◽  
Statistical Analyses ◽  
Striking Feature ◽  
Special Issue ◽  
Current State ◽  
Editorial Special Issue ◽  
Data Points ◽  
Robust Statistical Methods

AbstractStarting with 2020 volume, the journal Metron has decided to celebrate the centenary since its foundation with three special issues. This volume is dedicated to robust statistics. A striking feature of most applied statistical analyses is the use of methods that are well known to be sensitive to outliers or to other departures from the postulated model. Robust statistical methods provide useful tools for reducing this sensitivity, through the detection of the outliers by first fitting the majority of the data and then by flagging deviant data points. The six papers in this issue cover a wide orientation in all fields of robustness. This editorial first provides some facts about the history and current state of robust statistics and then summarizes the contents of each paper.

scholarly journals Spatial and spatio-temporal statistical analyses of retinal images: a review of methods and applications

2020 ◽  
Vol 5 (1) ◽  
pp. e000479
Author(s):  
Wenyue Zhu ◽  
Ruwanthi Kolamunnage-Dona ◽  
Yalin Zheng ◽  
Simon Harding ◽  
Gabriela Czanner
Keyword(s):  
Statistical Methods ◽  
Spatial Data ◽  
Temporal Analysis ◽  
Statistical Analyses ◽  
Retinal Images ◽  
Imaging Data ◽  
Spatial Correlations ◽  
Clinical Variables ◽  
Text Information ◽  
Spatio Temporal

BackgroundClinical research and management of retinal diseases greatly depend on the interpretation of retinal images and often longitudinally collected images. Retinal images provide context for spatial data, namely the location of specific pathologies within the retina. Longitudinally collected images can show how clinical events at one point can affect the retina over time. In this review, we aimed to assess statistical approaches to spatial and spatio-temporal data in retinal images. We also review the spatio-temporal modelling approaches used in other medical image types.MethodsWe conducted a comprehensive literature review of both spatial or spatio-temporal approaches and non-spatial approaches to the statistical analysis of retinal images. The key methodological and clinical characteristics of published papers were extracted. We also investigated whether clinical variables and spatial correlation were accounted for in the analysis.ResultsThirty-four papers that included retinal imaging data were identified for full-text information extraction. Only 11 (32.4%) papers used spatial or spatio-temporal statistical methods to analyse images, others (23 papers, 67.6%) used non-spatial methods. Twenty-eight (82.4%) papers reported images collected cross-sectionally, while 6 (17.6%) papers reported analyses on images collected longitudinally. In imaging areas outside of ophthalmology, 19 papers were identified with spatio-temporal analysis, and multiple statistical methods were recorded.ConclusionsIn future statistical analyses of retinal images, it will be beneficial to clearly define and report the spatial distributions studied, report the spatial correlations, combine imaging data with clinical variables into analysis if available, and clearly state the software or packages used.

Presenting Medical Statistics from Proposal to Publication

2013 ◽  
Author(s):  
Janet Peacock ◽  
Sally Kerry
Keyword(s):  
Statistical Methods ◽  
Journal Article ◽  
Real Data ◽  
Statistical Analyses ◽  
Medical Statistics ◽  
Wide Range ◽  
Good Presentation

Presenting Medical Statistics includes a wide range of statistical analyses, and all the statistical methods are illustrated using real data. Labelled figures show the Stata and SPSS commands needed to obtain the analyses, with indications of which information should be extracted from the output for reporting. The relevant results are then presented as for a report or journal article, to illustrate the principles of good presentation.

Assessing assumptions for statistical analyses in randomised clinical trials

2019 ◽  
Vol 24 (5) ◽  
pp. 185-189 ◽  
Author(s):  
Emil Eik Nielsen ◽  
Anders Kehlet Nørskov ◽  
Theis Lange ◽  
Lehana Thabane ◽  
Jørn Wetterslev ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Statistical Methods ◽  
Statistical Power ◽  
Current Practice ◽  
Statistical Analyses ◽  
Randomised Clinical Trials ◽  
Validity Of Results ◽  
Medical Journals ◽  
Statistical Assumptions

In order to ensure the validity of results of randomised clinical trials and under some circumstances to optimise statistical power, most statistical methods require validation of underlying statistical assumptions. The present paper describes how trialists in major medical journals report tests of underlying statistical assumptions when analysing results of randomised clinical trials. We also consider possible solutions how to improve current practice by adequate reporting of tests of underlying statistical assumptions. We conclude that there is a need to reach consensus on which underlying assumptions should be assessed, how these underlying assumptions should be assessed and what should be done if the underlying assumptions are violated.

scholarly journals El método compativo en ecología vegetal

2017 ◽  
pp. 37
Author(s):  
Eduardo Morales
Keyword(s):  
Statistical Analysis ◽  
Statistical Methods ◽  
Phylogenetic Relationships ◽  
Comparative Method ◽  
Comparative Methods ◽  
Statistical Analyses ◽  
Phenotypic Traits ◽  
The Third ◽  
Phylogenetic Inertia ◽  
Ancestral Character Estimation

In this paper a review of the uses of the comparative method in plant eco logy is presented. Particular attention is devoted to statistical methods that analyze variation in continuos phenotypic traits. The comparative method incorporates the phylogenetic relationships of the species in recognition that species usually do not provide independent points in statistical analysis because they share characteristics through descent from common ancestors. This review is divided in three sections. In the first one, the different statistical analysis that comprises the comparative method are presented, particular attention is devoted to: i] Evolutionary correlations, ii] phylogenetic inertia, and iii] ancestral character estimation. The second section presents the different papers that had applied these different methodologies, in both, origin al or reanalyzed data. Finally, in the third section the use of comparative methods to study adaptation and the debate between the use of phylogenetically based statistical methods and conventional statistical analyses are discussed.

scholarly journals Twinning Rates in Isolates

2016 ◽  
Vol 19 (6) ◽  
pp. 673-678 ◽  
Author(s):  
Johan Fellman
Keyword(s):  
General Population ◽  
Confidence Intervals ◽  
Statistical Methods ◽  
Extreme Values ◽  
Statistical Analyses ◽  
Isolated Populations ◽  
Southwestern Part ◽  
Variance Formula ◽  
Random Fluctuations ◽  
Mountain Villages

The aim of this study was to investigate the twinning rates (TWRs) in isolates relative to the TWRs in the surrounding populations. It is not uncommon that the TWR shows extreme values (high or low rates) within isolated subpopulations. Starting from the isolated populations of the Åland Islands in Finland (high rates), we enlarged our studies to other isolated subpopulations in other countries: the island of Gotland (high rates), the county of Älvsborg located in the southwestern part of Sweden (low rates), and mountain villages in Norway. In our statistical analyses, we paid special attention to the robustness of the variance formula of the TWR and to alternative confidence intervals for the TWR. Particularly, we show how to obtain the most precise confidence intervals for the twinning rates. These statistical methods are crucial when the extreme TWRs within subpopulations are compared with the TWRs within the general population. One must decide whether the differences are real or caused by random fluctuations within the small isolates.

scholarly journals Statistical Methods for Handling Observation Clustering in Sports Injury Surveillance

2019 ◽  
Vol 54 (11) ◽  
pp. 1192-1196 ◽  
Author(s):  
Avinash Chandran ◽  
Derek Brown ◽  
Aliza K. Nedimyer ◽  
Zachary Y. Kerr
Keyword(s):  
Statistical Methods ◽  
Analytical Methods ◽  
Sports Injury ◽  
Careful Consideration ◽  
Multiple Injury ◽  
Injury Surveillance ◽  
Statistical Analyses ◽  
Surveillance Data ◽  
Time Loss ◽  
Injury Reporting

Context Advances in sports injury-surveillance methods have made it possible to accommodate non–time-loss (NTL) injury reporting; however, the analysis of surveillance data now requires careful consideration of the nuances of NTL injury records. Background Injury-surveillance mechanisms that record NTL injuries are more likely to contain multiple injury records per athlete. These must be handled appropriately in statistical analyses to make methodologically sound inferences. Methods We simulated datasets of NTL injuries using varying degrees of observation clustering and compared the inferences made using traditional techniques with those made after accounting for clustering in computations of injury proportion ratios. Results Inappropriate handling of even moderate clustering resulted in flawed inferences in 10% to 12% of our simulations. We observed greater bias in our estimates as the degree of clustering increased. Conclusions We urge investigators to carefully consider observation clustering and adapt analytical methods to accommodate the evolving sophistication of surveillance.

scholarly journals European Jurassic floras: statistics and palaeoenvironmental proxies

2014 ◽  
Vol 54 (2) ◽  
pp. 173-195 ◽  
Author(s):  
Maria Barbacka ◽  
Emese Bodor ◽  
Agata Jarzynka ◽  
Evelyn Kustatscher ◽  
Grzegorz Pacyna ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
United Kingdom ◽  
Environmental Factors ◽  
Statistical Methods ◽  
Species Level ◽  
Statistical Analyses ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
The United Kingdom ◽  
Plant Groups

Abstract The Jurassic floras of Europe show considerable diversity. To examine the extent of this diversity and its possible causes we used multivariate statistical methods (cluster analysis, PCA, NMDS) to compare all significant Jurassic floras in Europe. Data were based on 770 taxa from 46 fossiliferous occurrences (25 units) from France, Germany, Greenland, Hungary, Italy, Norway, Poland, Romania, Scotland, Serbia, Sweden, Switzerland, and the United Kingdom. Statistical analyses were applied at species level and genus level, and also performed for the major plant groups. The genus cladograms show affinities between different localities based on environmental factors, while the cladograms based on species affinities indicate only taxonomical correlations. The study shows that locality age does not seem to be of paramount importance for floral composition.

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