Statistical Aspects Research Articles

Integrative functional linear model for genome-wide association studies with multiple traits.

In recent biomedical research, genome-wide association studies (GWAS) have demonstrated great success in investigating the genetic architecture of human diseases. For many complex diseases, multiple correlated traits have been collected. However, most of the existing GWAS are still limited because they analyze each trait separately without considering their correlations and suffer from a lack of sufficient information. Moreover, the high dimensionality of single nucleotide polymorphism (SNP) data still poses tremendous challenges to statistical methods, in both theoretical and practical aspects. In this article, we innovatively propose an integrative functional linear model for GWAS with multiple traits. This study is the first to approximate SNPs as functional objects in a joint model of multiple traits with penalization techniques. It effectively accommodates the high dimensionality of SNPs and correlations among multiple traits to facilitate information borrowing. Our extensive simulation studies demonstrate the satisfactory performance of the proposed method in the identification and estimation of disease-associated genetic variants, compared to four alternatives. The analysis of type 2 diabetes data leads to biologically meaningful findings with good prediction accuracy and selection stability.

Discussion on A high‐resolution bilevel skew‐t stochastic generator for assessing Saudi Arabia's wind energy resources

AbstractWe provide a detailed discussion on the analysis presented by Tagle and co‐authors, who suggested an approach to improve earlier models for handling non‐Gaussianity in spatial wind field speed data by simplifying the model formulation to better accommodate large data sets. Our discussion focuses on the energy and socio‐economic context of wind potential assessment in Saudi Arabia – an oil‐rich country, statistical aspects associated with wind field forecasting, and the prediction of the wind electricity production potential from the wind field forecast.

A switched chaotic encryption scheme using multi-mode generalized modified transition map

This paper presents a multi-mode generalized modified transition chaotic map and a switched chaotic encryption scheme based on it. Eight different modes of operation can be selected based on the map graph (concave or convex), the range modification procedure (shrinking or widening) and the sign of one of its independent parameters. The generalization and modification preserve the controllability and continuous chaotic behavior properties, respectively. For the same encryption key and map equation, multi-mode operation occurs through switching between four alternatives of the dependent parameters. Switching between various modes is shown to overpass single modes in randomness by investigating the frequency distribution, autocorrelation function, histograms and 0-1 test. In addition, most bits, 75%, of the scaled and quantized chaotic output are shown to be suitable for pseudo-random number generation based on National Institute of Standards & Technology (NIST) statistical test suite. The performance of the proposed scheme is validated for several speech, grayscale and RGB images through various evaluation criteria, where good results are achieved. These criteria include basic perceptual and statistical aspects, the more advanced NIST tests, key sensitivity and robustness against brute force and differential attacks. The efficiency of the scheme is improved by up to three or six times faster than encrypting one sample or one channel of a pixel at a time for speech and image inputs, respectively. The proposed scheme demonstrates good security and efficiency with more applied evaluation criteria compared to similar recent works.

Modern Adaptive Randomized Clinical Trials: Statistical and Practical Aspects

Modern Adaptive Randomized Clinical Trials: Statistical and Practical Aspects

A Replication Crisis in Methodological Research?

AbstractStatisticians have been keen to critique statistical aspects of the “replication crisis” in other scientific disciplines. But new statistical tools are often published and promoted without any thought to replicability. This needs to change, argue Anne-Laure Boulesteix, Sabine Hoffmann, Alethea Charlton and Heidi Seibold

Transformation of migration processes within Eurasian economic union: challenges and new opportunities

This paper examines the transformation of the migration process within the Eurasian Economic Union, taking into account legislative, statistical and managerial aspects. The study showed that, despite the positive development trends, the continuing imbalance within both economic and political indicators remains a serious internal challenge for the EEU. Thus, the task of Eurasian integration is the effective economic development of the Union countries and increasing their competitiveness. It also is necessary to take effective measures aimed at unifying the migration legislation of the participating countries and creating a single labor market.

A protocol for a systematic review investigating the factors influencing the statistical planning, design, conduct, analysis and reporting of trials.

Trials can be defined as prospective human research studies to test the effectiveness and safety of interventions, such as medications, surgeries, medical devices and other interventions for the management of patient care. Statistics is an important and powerful tool in trials. Inappropriately designed trials and/or inappropriate statistical analysis produce unreliable results, with limited clinical use. The aim of this systematic literature review is to identify, describe and synthesise factors contributing to or influencing the statistical planning, design, conduct, analysis and reporting of trials. This protocol will describe the methodological approach taken for the following: conducting a systematic and comprehensive search for relevant articles, applying eligibility criteria for the inclusion of such articles, extracting data and information, appraising the quality of the articles, and thematically synthesizing the data to illuminate the key factors influencing statistical aspects of trials.

Data monitoring committees for clinical trials evaluating treatments of COVID-19

The first cases of coronavirus disease 2019 (COVID-19) were reported in December 2019 and the outbreak of SARS-CoV-2 was declared a pandemic in March 2020 by the World Health Organization. This sparked a plethora of investigations into diagnostics and vaccination for SARS-CoV-2, as well as treatments for COVID-19. Since COVID-19 is a severe disease associated with a high mortality, clinical trials in this disease should be monitored by a data monitoring committee (DMC), also known as data safety monitoring board (DSMB). DMCs in this indication face a number of challenges including fast recruitment requiring an unusually high frequency of safety reviews, more frequent use of complex designs and virtually no prior experience with the disease. In this paper, we provide a perspective on the work of DMCs for clinical trials of treatments for COVID-19. More specifically, we discuss organizational aspects of setting up and running DMCs for COVID-19 trials, in particular for trials with more complex designs such as platform trials or adaptive designs. Furthermore, statistical aspects of monitoring clinical trials of treatments for COVID-19 are considered. Some recommendations are made regarding the presentation of the data, stopping rules for safety monitoring and the use of external data. The proposed stopping boundaries are assessed in a simulation study motivated by clinical trials in COVID-19.

A protocol for a systematic review investigating the factors influencing the statistical planning, design, conduct, analysis and reporting of trials

Trials can be defined as prospective human research studies to test the effectiveness and safety of interventions, such as medications, surgeries, medical devices and other interventions for the management of patient care. Statistics is an important and powerful tool in trials. Inappropriately designed trials and/or inappropriate statistical analysis produce unreliable results, with limited clinical use. The aim of this systematic literature review is to identify, describe and synthesise factors contributing to or influencing the statistical planning, design, conduct, analysis and reporting of trials. This protocol will describe the methodological approach taken for the following: conducting a systematic and comprehensive search for relevant articles, applying eligibility criteria for the inclusion of such articles, extracting data and information, appraising the quality of the articles, and thematically synthesizing the data to illuminate the key factors influencing statistical aspects of trials.

Design issues when assessing a topical pharmacological treatment in dermatology

Introduction Skin diseases are common, with an estimated overall prevalence of 27% to 55% [1] , [2] . Topical treatments are commonly prescribed and are usually the first-line treatment. Patients may have a single lesion, multiple lesions or extensive body surface areas [3] Variability in the topography of the skin lesions offers different possibilities for randomized controlled trials (RCTs) evaluating topical treatments. Randomization units may be patients or lesions; all lesions may be assessed or just one. We may perform a classical individually RCT, a cluster RCT (in which clusters are actually patients, and units within clusters are lesions), or a within-person RCT. Each design has its own methodological and statistical constraints and may not be used for all skin diseases. Methods This work aimed to: – identify and discuss key methodological aspects for each design; – report sample size consequences of selecting a particular design; – illustrate statistical aspects using data from an RCT evaluating topical tacrolimus in vitiligo [4] . Results Methodological points to consider are those listed in the CONSORT statement and SPIRIT checklist. Each point was discussed by a group of three of us. Then a larger working group of seven dermatologists and statisticians who have been involved in the planning, analysis and reporting of results of dermatologic RCTs attended a 1-day meeting to discuss each potential key methodological point. For sample size issues, graphs were drawn to compare the different designs and a simulation study based on real data was used to illustrate these results. Conclusion Our work will help dermatologists and methodologists design future RCTs evaluating a topical treatment.

A Study on Fight Against COVID-19 from Latest Technological Intervention.

Uncontrolled spread of pandemic COVID-19 in India and across the globe over several months, created an impact as never before any pandemic would have created. This certainly demands a technological intervention from all possibility to overcome the situation and lead a normal life as early as possible. AI/Machine learning responds to the situation, through inspecting different aspects of the pandemic. This paper analyses and studies those aspects, (I) Quarantine and statistical aspect: Quarantine potentially affected candidates (person who is in touch, travel history) through Data analytics/Machine learning. (II) Diagnosis and Treatment aspect: Early detection and fast treatment will save lives. Diagnosis using deep learning assists radiologist from saving their effort and time to a greater extent and arrives faster conclusion. (III) Prevention aspect: Monitoring and enforce social distancing through visual social distancing using deep learning and Computer vision.

A Graph-Based Tag Recommendation for Just Abstracted Scientific Articles Tagging

Tags, when properly assigned to limited access papers, help users to estimate their relevance. This paper introduces a new approach for the selection of relevant tags as well as a recommendation for scientific papers tagging. The approach defines the relatedness between the tags attributed by users and the concepts extracted from the available sections of scientific papers based on statistical, structural and semantic aspects. Two different term-based graphs ([Formula: see text]-graph and [Formula: see text]-graph) were generated whose vertices indicate the terms and the edges represent the relatedness score between these terms. In addition, two algorithms were implemented to select and recommend the relevant tags: the neighbor-algorithm and the best-path-algorithm. The results of the experiments performed on a CiteULike collection of tagged papers show significant improvements only for the tagging of abstracted scientific articles. The approach was evaluated by referring to the full text of the papers with expert evaluation and comparing the tags generated by CiteULike users. Using the neighbor-algorithm, 80% of the top 10 recommended tags based on [Formula: see text]-graph and 76% of the top 10 recommended tags based on the [Formula: see text]-graph were relevant. While only 62% of those recommended by CiteULike users were relevant. The best-path-algorithm gave the best results in the top 20 and top 30 recommended tags and this in comparison with the tags recommended by the neighbor-algorithm and the tags assigned by CiteULike users.

CATASTROPHE AND RATIONAL POLICY: CASE OF NATIONAL SECURITY

Predicting catastrophes involves heavy‐tailed distributions with no mean, eluding proactive policy as expected cost‐benefit analysis fails. We study US government counterterrorism policy, given heightened risk of terrorism. But terrorism also involves human behavior. We synthesize the behavioral and statistical aspects in an adversary‐defender game. Calibration to extensive data shows that where a Weibull distribution is the best predictor, US counterterrorism policy is rational (and optimal). Here, we estimate the adversary's unobserved variables, e.g., difficulty of an attack. We also find cases where the best predictor is a Generalized‐Pareto with no finite mean and rational policy fails. Here, we offer “work‐arounds”. (JEL H56, D81, C46)

A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations

Insider threat has become a widely accepted issue and one of the major challenges in cybersecurity. This phenomenon indicates that threats require special detection systems, methods, and tools, which entail the ability to facilitate accurate and fast detection of a malicious insider. Several studies on insider threat detection and related areas in dealing with this issue have been proposed. Various studies aimed to deepen the conceptual understanding of insider threats. However, there are many limitations, such as a lack of real cases, biases in making conclusions, which are a major concern and remain unclear, and the lack of a study that surveys insider threats from many different perspectives and focuses on the theoretical, technical, and statistical aspects of insider threats. The survey aims to present a taxonomy of contemporary insider types, access, level, motivation, insider profiling, effect security property, and methods used by attackers to conduct attacks and a review of notable recent works on insider threat detection, which covers the analyzed behaviors, machine-learning techniques, dataset, detection methodology, and evaluation metrics. Several real cases of insider threats have been analyzed to provide statistical information about insiders. In addition, this survey highlights the challenges faced by other researchers and provides recommendations to minimize obstacles.

Statistical aspects of nuclear mass models

We study the information content of nuclear masses from the perspective of global models of nuclear binding energies. To this end, we employ a number of statistical methods and diagnostic tools, including Bayesian calibration, Bayesian model averaging, chi-square correlation analysis, principal component analysis and empirical coverage probability. Using a Bayesian framework, we investigate the structure of the four-parameter liquid drop model by considering discrepant mass domains for calibration. We then use the chi-square correlation framework to analyze the 14-parameter Skyrme energy density functional calibrated using homogeneous and heterogeneous datasets. We show that quite a dramatic parameter reduction can be achieved in both cases. The advantage of Bayesian model averaging for improving uncertainty quantification is demonstrated. The statistical approaches used are pedagogically described; in this context this work can serve as a guide for future applications.

Sparse recovery via nonconvex regularized [formula omitted]-estimators over [formula omitted]-balls

The recovery properties of nonconvex regularized M-estimators are analysed, under the general sparsity assumption on the true parameter. In the statistical aspect, the recovery bound for any stationary point of the nonconvex regularized M-estimator is established under some regularity conditions. In the computational aspect, the proximal gradient method is used to solve the nonconvex optimization problem and is proved to achieve a linear convergence rate, by virtue of a slight decomposition of the objective function. In particular, for commonly-used regularizers such as SCAD and MCP, a simpler decomposition is applicable thanks to the assumption on the regularizer, which helps to construct the estimator with better recovery performance. In the aspect of application, theoretical consequences are obtained on the corrupted error-in-variables linear regression model by verifying the required conditions. Finally, statistical and computational results as well as advantages of the assumptions are demonstrated by several numerical experiments. Simulation results show remarkable consistency with the theory under high-dimensional scaling.

Statistical Aspects of Community Health and Nutrition

One of the most common problems with the area of health and nutritional research is the limited number of quality books available that can provide research methodology, health indicators and their trend in a single volume. Statistical Aspects of Community Health and Nutrition is a one of the problem-based text book which completely fulfils the gap and stands to our expectations.
 This book is a single but comprehensive resource on maternal and infant mortality, anemia especially in adolescents and women in reproductive age group and, their causes, prevention, evaluation and validation methods including 30 clusters design, logistic regression and findings of recent relevant studies. Despite above, author also discusses the food insecurity and hunger, tuberculosis, influenza like prevalent diseases, their hotspot and available estimates, techniques for analyzing multiple response data, and small area estimation.

A method for forensic gasoline comparison in fire debris samples: A numerical likelihood ratio system

In many arson cases gasoline is used as a means to start the fire. In this paper results are presented for a likelihood-ratio (LR) system aimed at comparing gasoline traces from fire debris to a reference gasoline. The LR-system is able to deal with disturbing effects caused by burning and exposure to surroundings: pyrolysis products, preferential adsorption and evaporation. This paper focusses on the criminalistic and statistical aspects of the design of the LR-system, and presents results on performance of the LR-system. The details of trace gasoline recovery from fire debris will be presented in an accompanying paper.Validation and performance measures show that this system gives well-calibrated LRs for comparisons involving trace samples with a spread in quantity of gasoline, evaporation levels, and matrices that are typically encountered in casework. Rates of misleading evidence are less than 3.5%.We conclude that, despite limitations in experimental design, this LR-system can be useful to the comparison of gasoline profiles in casework practice.

Corneal cross-linking versus standard care in children with keratoconus \u2013 a randomised, multicentre, observer-masked trial of efficacy and safety (KERALINK): a statistical analysis plan

BackgroundThe KERALINK trial tests the hypothesis that corneal cross-linking (CXL) treatment reduces the progression of keratoconus in comparison to standard care in patients aged 10–16 years. This article describes the statistical analysis plan for this trial as an update to the published protocol. It is written before the end of the patient follow-up, while the outcome of the trial is still unknown.Design and methodsKERALINK is a randomised controlled, observer-masked, multicentre trial in progressive keratoconus comparing epithelium-off CXL with standard care, including spectacles or contact lenses as necessary for best-corrected acuity. Keratoconus is a disorder of the shape of the cornea in which the normally round dome-shaped clear front window of the eye (cornea) thins progressively leading to a cone-like bulge. This impairs the ability of the eye to focus properly, causing reduced vision which requires spectacle or contact lens wear or, in a minority of patients, eventually corneal replacement by a transplant for best vision. The primary outcome measure is the between-group difference in K2 at 18 months adjusted for K2 at baseline examination. K2 is the value of the steepest corneal meridian as measured on Pentacam topography. Secondary outcomes are keratoconus progression, time to keratoconus progression, visual acuity, refraction, apical corneal thickness and adverse events. Patient-reported effects will be explored by questionnaires. We describe in detail the statistical aspects of KERALINK: the outcome measures, the sample size calculation, general analysis principles, the planned descriptive statistics and statistical models, and planned subgroup and sensitivity analyses.DiscussionThe KERALINK statistical analysis will provide comprehensive and precise information on the relative effectiveness of the two treatments. The plan will be implemented in May 2020 when follow-up for the trial is completed.Trial registrationEudraCT, 2016-001460-11. Registered on 19 May 2016.

A protocol for a systematic review investigating the factors influencing the statistical planning, design, conduct, analysis and reporting of clinical trials.

Clinical trials can be defined as prospective human research studies to test the effectiveness and safety of interventions, such as medications, surgeries, medical devices and other interventions for the management of patient care. Statistics is an important and powerful tool in clinical trials. Inappropriately designed trials and/or inappropriate statistical analysis produce unreliable results, with limited clinical use. The aim of this systematic literature review is to identify, describe and synthesise factors contributing to or influencing the statistical planning, design, conduct, analysis and reporting of clinical trials. This protocol will describe the methodological approach taken for the following: conducting a systematic and comprehensive search for relevant articles, applying eligibility criteria for the inclusion of such articles, extracting data and information, appraising the quality of the articles, and thematically synthesizing the data to illuminate the key factors influencing statistical aspects of clinical trials.