Correct Statistical Analysis Research Articles

Quantinar: a blockchain peer-to-peer ecosystem for modern data analytics

AbstractThe power of data and correct statistical analysis has never been more prevalent. Academics and practitioners require nowadays an accurate application of quantitative methods. Yet many branches are subject to a crisis of integrity, which is shown in an improper use of statistical models, p-hacking, HARKing, or failure to replicate results. We propose the use of a Peer-to-Peer (P2P) ecosystem based on a blockchain network, Quantinar, to support quantitative analytics knowledge paired with code in the form of Quantlets or software snippets. The integration of blockchain technology allows Quantinar to ensure fully transparent and reproducible scientific research.

Multidimensional statistical analysis of social inequalities in Italy

The analysis of social inequalities is a topic of current interest and is studied as a factor in the evolution and measurement of the level of well-being. A fundamental prerequisite for a correct statistical analysis of this phenomenon is the need to share a univocal definition of the concept of social inequalities. This work starts from the need to identify territorial areas and/or population subgroups characterized by situations of hardship or strong social exclusion through the construction of indicators that can estimate situations of social inequalities in small areas. Scientific research options have been oriented towards the establishment of a multidimensional approach, sometimes renouncing dichotomous logic to go as far as fuzzy classifications in which each unit simultaneously belongs and does not belong to the selected category. Multidimensional statistical analysis methodologies (TFR method) and Density Based Spatial Clustering methods (DBSCAN) will therefore be used to aggregate adjacent spatial units with high intensity of social inequalities.

Economic analysis of permanent plants by categories of farms of the Kyrgyz Republic

The article gives an assessment of the current state of agricultural crops and perennial plantations in terms of qualitative and quantitative indicators, as well as a correct statistical analysis by category of farms in the Kyrgyz Republic. The purpose of the article is to study the analytical support of the process of managing perennial plantations, in particular, to identify the methodological features of the analysis of perennial plantations. Development of a system of analytical indicators to assess the process of reproduction in the field of perennial plantations, as well as statistical analysis of arable land of agricultural crops and perennial plantations by category of farms of the Kyrgyz Republic. In the future, the horticulture sector, especially fruit and berry crops in all regions, is more favorable for the development of the economy of the republic. The efficiency of the use of agricultural land and the development of perennial plantations, fruit and berry crops are considered. This suggests that the bulk of agricultural land is privately owned, and peasant (farmer) households aim to use their private land for perennial plantings to produce the most cost-effective and efficient crops.

The Efficacy of Deep Breathing Techniques in Patients with Elevated Blood Pressure and Hypertension: A Review of the Literature

Introduction: Increased blood pressure (BP) is among the top modifiable causes of cardiovascular disease risk. Non- pharmacological interventions in the prevention and management of elevated BP and hypertension have increased in popularity in recent years. Deep breathing techniques (DBT) are mindfulness stress reduction strategies that target the autonomous nervous system, increase vagal tone, and reduce BP levels. Inconsistent evidence exists in the efficacy of DB techniques in BP control. This review of the literature aims to assess the overall BP lowering efficacy of DBT in hypertension, compare the efficacy of device-guided versus non-device-guided DBT, and assess the safety and adherence rate of DBT, through systematic search and review of randomized controlled trials. Methods: We searched Randomized clinical trials (RCT) in PubMed and Cochrane databases. The search terms were divided into three-term groups: a) hypertension, b) deep breathing techniques, and c) trials. Then, the list of articles was divided into five groups, each with two to three reviewers, to verify every study met the inclusion and exclusion criteria by reading the title and the abstract. Whenever necessary, we downloaded articles to verify adherence by reading the methods, results, and discussions. Studies having two or more votes were selected. Next, all selected articles were downloaded and read to analyze findings and effect sizes and to confirm a correct methodology and statistical analysis. Results: We selected 4 of 119 articles. There is a conflict in the literature; one study affirms no overall difference between DBT and control, two studies affirm that there are statistically significant differences, and one study reports no difference in office BP but statistical difference in “awake, at home” monitoring in the control group. Thus large-scale, high-quality clinical trials are needed to critically assess the effectiveness of long-term DBT in the management of HTN.

Pose detection using deep learning for gait assessment

This approach of gait analysis helps in solving the problems associated with the health of aged people by concentrating on the frailty and senility syndromes that affect elderly individuals. Deterioration of cognitive and motor abilities is a common result of ageing and has an effect on elderly people's quality of life. Some studies have connected these changes in gait patterns to the decline in cognitive and motor function. As a result, gait analysis is a useful tool for diagnosing senility and frailty diseases. By transferring the pose estimation data to a correct statistical analysis, gait analysis can be carried out utilizing machine learning and computer vision approach, allowing physiotherapists to analyse and treat patients accordingly. The main problem observed is that physiotherapists will analyse stroke patients' walking patterns on their own. As a result, this approach solves the problem by providing an accurate result of the observation. As everything is computerised, proper treatment can be provided with accurate data. A physiotherapist could also use machine learning classification techniques to analyse and classify the observed disorder and correct it. Our study mainly focuses on using machine learning approach instead of traditional sensor based or consultation just by vision. This will serve as a newer and better proposition of the same.

Statistical Errors in “How Graduate Students With Vocal Fry Are Perceived by Speech-Language Pathologists”

Purpose: Perspectives of the ASHA Special Interest Groups recently published a study (Gallena & Pinto, 2021) that investigated how graduate students with vocal fry are perceived by speech-language pathologists. The statistical analysis of the data presented in this study contains errors. The purpose of this letter is to describe these errors, outline practices that may facilitate error detection prior to publication, and position these errors in the broader context of vocal quality biases that may influence hiring and supervision decisions among speech-language pathologists. Conclusions: The statistical errors in Gallena and Pinto (2021) reflected a violation of the assumption of independence required by the employed statistical tests and inaccurate calculation of effect size measures. The severity of these errors makes it difficult to determine whether the reported results would hold if the correct statistical analyses were applied to the data and, thus, challenge an understanding of the degree to which vocal quality may influence perceived speaker traits and hiring or supervision decisions. Open science practices, including the Registered Report model of publication and open access to data and analysis pipelines during the review process, provide mechanisms to support error detection prior to publication.

Diagnostic value of motor evoked potential parameters in patients with Parkinson’s disease stage II

The aim of our study was to identify the most informative parameters of motor evoked potential (MEP) based on clinical and neurophysiological comparisons in patients with Parkinson’s disease (PD) stage II. Materials and methods. The study included 90 patients aged 45 to 75 years with stage II PD according to Hoehn–Yahr. Examination of patients was performed according to the following scheme: clinical and neurological examination with MDS UPDRS scale and neurophysiological examination to determine the latency, amplitude and duration of MEP after 4 tests with a gradual increase of magnetic field induction. Our study involved patients with predominantly right and left-sided motor symptoms, so for correct statistical analysis, the dominant side was considered as the debut side or the side with more pronounced motor symptoms, the subdominant side was considered as the opposite one. Results. In cases of patients with stage II PD the MEP latency significantly decreased, and the amplitude and duration of MEP significantly increased in samples with increasing magnetic field induction in the right and left hemispheres of the brain. It was found significant positive moderate correlation between UPDRS part III total score and MEP latency in the ipsilateral premotor cortex to the dominant side of motor symptoms with samples of magnetic induction (1.1 Tl – r = 0.34, P < 0.05; 1.32 Tl – r = 0.32, P < 0.05; 1.76 Tl – r = 0.31, P < 0.05). Also positive mild correlation was found between MEP latency in the ipsilateral premotor cortex to the subdominant side of motor symptoms and UPDRS part III total score (1.54 Tl – r = 0.22, P < 0.05; 1.76 Tl – r = 0.29, P < 0.05). Mild positive correlation (r = 0.29, P < 0.05) was found between MEP duration of ipsilateral premotor cortex to the subdominant side of motor symptoms and UPDRS part III total score in sample with 1.54 Tl magnetic induction. No significant correlations were found to the amplitude parameter and the severity of clinical symptoms in investigated patients with PD stage II. Conclusions. The most informative neurophysiological indicators in patients with PD stage II are the MEP latencies. The MEP latencies of the premotor cortex, which is ipsilateral to the side with more pronounced motor symptoms, have a particularly close relationship with UPDRS part III total score in the samples with 1.10–1.76 Tl magnetic inductions (r = 0.31–0.34, P ˂ 0.05). The MEP duration of premotor cortex, which is ipsilateral to the side with less pronounced motor symptoms, is most closely associated with the severity of motor manifestations on the subdominant side in patients with PD stage II (r = 0.27, P ˂ 0.05).

Towards Good Statistical Practice. CORSTAN Validated Questionnaire for Assessing the Correctness of Statistical Analysis in Medical Research.

In evidence-based medicine, the research methodology is determined by the risks of systematic biases and incorrect data analysis. Minimizing both risks increases the internal validity of the study. There are numerous recommendations and guidelines for data analysis and reporting, but the international community has not yet developed a questionnaire for reviewers to assess the quality of statistical analysis. To develop a tool for formalized assessment of the quality of statistical analysis presented in scientific medical publications. The questionnaire was developed based on the authors' decades of experience in statistical data analysis and reviewing the statistical aspects of biomedical articles and dissertations. The SAMPL guidelines, ICH E9, and other guidelines were taken into account when developing the questionnaire. Internal validation of the questionnaire was based on an independent assessment by two experts of 20 randomly selected articles on randomized controlled trials (RCTs) from elibrary.ru, and further statistical analysis of the agreement of experts' conclusions. The CORSTAN (CORrect STatistical ANalysis) questionnaire was developed, which consists of two parts: the first part (10 questions) is intended for evaluating studies of any designs, while the second (following eight questions) is for additional assessment of RCTs. A stratification of the risk of incorrect statistical analysis is proposed. The evaluation of the questionnaire's internal validity showed its substantial and almost perfect agreement for each question and each article both in the sum of points and risk level. The use of the questionnaire will simplify and harmonize the statistical review of publications and manuscripts in various institutions - scientific journals, dissertation boards, etc. The questionnaire can also be helpful for authors during preparing manuscripts; it will also help improve the quality of publications and research itself. We plan to improve the questionnaire as we gain experience in its application.

Statistical analysis of cytogenetic data

Using the data obtained in a cytogenetic study as an example, we consider the typical errors that are made when performing statistical analysis. Widespread but flawed statistical analysis inevitably produces biased results and increases the likelihood of incorrect scientific conclusions. Errors occur due to not taking into account the study design and the structure of the analyzed data. The article shows how the numerical imbalance of the data set leads to a bias in the result. Using a dataset as an example, it explains how to balance the complex. It shows the advantage of presenting sample indicators with confidence intervals instead of statistical errors. Attention is drawn to the need to take into account the size of the analyzed shares when choosing a statistical method. It shows how the same data set can be analyzed in different ways depending on the purpose of the study. The algorithm of correct statistical analysis and the form of the tabular presentation of the results are described.
 Keywords: data structure, numerically unbalanced complex, confidence interval.

Implementing an automated monitoring process in a digital, longitudinal observational cohort study

BackgroundClinical data collection requires correct and complete data sets in order to perform correct statistical analysis and draw valid conclusions. While in randomized clinical trials much effort concentrates on data monitoring, this is rarely the case in observational studies- due to high numbers of cases and often-restricted resources. We have developed a valid and cost-effective monitoring tool, which can substantially contribute to an increased data quality in observational research.MethodsAn automated digital monitoring system for cohort studies developed by the German Rheumatism Research Centre (DRFZ) was tested within the disease register RABBIT-SpA, a longitudinal observational study including patients with axial spondyloarthritis and psoriatic arthritis. Physicians and patients complete electronic case report forms (eCRF) twice a year for up to 10 years. Automatic plausibility checks were implemented to verify all data after entry into the eCRF. To identify conflicts that cannot be found by this approach, all possible conflicts were compiled into a catalog. This “conflict catalog” was used to create queries, which are displayed as part of the eCRF. The proportion of queried eCRFs and responses were analyzed by descriptive methods. For the analysis of responses, the type of conflict was assigned to either a single conflict only (affecting individual items) or a conflict that required the entire eCRF to be queried.ResultsData from 1883 patients was analyzed. A total of n = 3145 eCRFs submitted between baseline (T0) and T3 (12 months) had conflicts (40–64%). Fifty-six to 100% of the queries regarding eCRFs that were completely missing were answered. A mean of 1.4 to 2.4 single conflicts occurred per eCRF, of which 59–69% were answered. The most common missing values were CRP, ESR, Schober’s test, data on systemic glucocorticoid therapy, and presence of enthesitis.ConclusionProviding high data quality in large observational cohort studies is a major challenge, which requires careful monitoring. An automated monitoring process was successfully implemented and well accepted by the study centers. Two thirds of the queries were answered with new data. While conventional manual monitoring is resource-intensive and may itself create new sources of errors, automated processes are a convenient way to augment data quality.

SPT based determination of undrained shear strength: Regression models and machine learning

The purpose of this study is the accurate prediction of undrained shear strength using Standard Penetration Test results and soil consistency indices, such as water content and Atterberg limits. With this study, along with the conventional methods of simple and multiple linear regression models, three machine learning algorithms, random forest, gradient boosting and stacked models, are developed for prediction of undrained shear strength. These models are employed on a relatively large data set from different projects around Turkey covering 230 observations. As an improvement over the available studies in literature, this study utilizes correct statistical analyses techniques on a relatively large database, such as using a train/test split on the data set to avoid overfitting of the developed models. Furthermore, the validity and consistency of the prediction results are ensured with the correct use of statistical measures like p-value and cross-validation which were missing in previous studies. To compare the performances of the models developed in this study with the prior ones existing in literature, all models were applied on the test data set and their performances are evaluated in terms of the resulting root mean squared error (RMSE) values and coefficient of determination (R2). Accordingly, the models developed in this study demonstrate superior prediction capabilities compared to all of the prior studies. Moreover, to facilitate the use of machine learning algorithms for prediction purposes, entire source code prepared for this study and the collected data set are provided as supplements of this study.

Using Near-infrared Fluorescence and High-resolution Scanning to Measure Protein Expression in the Rodent Brain.

Neuroscience is the study of how cells in the brain mediate various functions. Measuring protein expression in neurons and glia is critical for the study of neuroscience as cellular function is determined by the composition and activity of cellular proteins. In this article, we describe how immunocytochemistry can be combined with near-infrared high-resolution scanning to provide a semi-quantitative measure of protein expression in distinct brain regions. This technique can be used for single or double protein expression in the same brain region. Measuring proteins in this fashion can be used to obtain a relative change in protein expression with an experimental manipulation, molecular signature of learning and memory, activity in molecular pathways, and neural activity in multiple brain regions. Using the correct proteins and statistical analysis, functional connectivity among brain regions can be determined as well. Given the ease of implementing immunocytochemistry in a laboratory, using immunocytochemistry with near-infrared high-resolution scanning can expand the ability of the neuroscientist to examine neurobiological processes at a systems level.

Career perspective: Kenneth J. Collins

A career interest in thermoregulation research has included wide contrasts in the subjects of enquiry, extending from heat stroke to hypothermia, special investigations in many different purpose-built climatic chambers, laboratory-based biomedical studies together with hospital practice, and field work in tropical climates to physiological surveys on urban populations in temperate environments. The scientific process and need to focus on careful planning of experiments, using the most appropriate methods, selecting the right controls and eventually applying correct statistical analysis do not always follow a smooth transition, as illustrated in this account. The result of endeavour to resolve a human environmental problem, however, is greatly satisfying, and sometimes becomes a unique experience when the solution reveals new fundamental facts.

Do 45% of College Students Lack Critical Thinking Skills? Revisiting a Central Conclusion of Academically Adrift

The educational literature, the popular press, and educated laypeople have all echoed a conclusion from the book Academically Adrift by Richard Arum and Josipa Roksa (which has now become received wisdom), namely, that 45% of college students showed no significant gains in critical thinking skills. Similar results were reported by Pascarella, Blaich, Martin, and Hanson after the publication of Arum and Roksa's book in 2011. However, these authors' statistical tests were conducted incorrectly, and therefore this 45% finding is fundamentally untrue. We demonstrate that a correct statistical analysis would have found that far fewer students show significant gains in critical thinking. However, this does not reflect on student learning; instead, it reflects on how hard it is to find a statistically significant result when assessing student change on a student‐by‐student basis. This article discusses valid methods for testing the significance of gain scores of individual students.

Significance of non-parametric statistical tests for comparison of classifiers over multiple datasets

In machine learning, generation of new algorithms or, in most cases, minor amendment of the existing ones is a common task. In such cases, a rigorous and correct statistical analysis of the results of different algorithms is necessary in order to select the exact techniques depending on the problem to be solved. The main inconvenience related to this necessity is the absence of proper compilation of statistical techniques. In this paper, we propose the use of two important non-parametric statistical tests, namely, Wilcoxon signed rank test for comparison of two classifiers and Friedman test with the corresponding post-hoc tests for comparison of multiple classifiers over multiple datasets. We also introduce a new variant of non-parametric test known as Scheffe's test for locating unequal pairs of means of performances of multiple classifiers when the given datasets are of unequal sizes. The parametric tests, which were previously being used for comparing multiple classifiers, have also been described in brief. The proposed non-parametric tests have also been applied on the classification results on ten real-problem datasets taken from the UCI Machine Learning Database Repository http://www.ics.uci.edu/mlearn Valdovinos and Sanchez, 2009 as case studies.

Functional MRI in clinical practice: Assessment of language and motor for pre-surgical planning.

Functional magnetic resonance imaging (fMRI) has been widely used for pre-neurosurgical planning and may eventually become a routine pre-surgical imaging modality. The validity of fMRI for clinical application depends on various factors such as proper task selection, correct statistical analysis and threshold setting with appropriate patient cooperation. This study was performed on 40 patients with different types of brain mass lesions or hippocampal sclerosis to assess the role of appropriate patient selection for achieving a reliable fMRI result. Accurate and reproducible fMRI strongly depends on the patient's cooperation. In this study we observed that a pre-test mock fMRI session held by a radiologist may help to predict which patients are more or less suitable candidates for fMRI.

Systematic vibration thermodynamic properties of bromine

Based on the analysis of the maturity and finiteness of vibrational levels of bromine molecule in ground state and evaluating the effect on statistical computation, according to the elementary principles of quantum statistical theorem, using the full set of bromine molecular vibrational levels determined with algebra method, the statistical contribution for bromine systematical macroscopic thermodynamic properties is discussed. Thermodynamic state functions Helmholtz free energy, entropy and observable vibration heat capacity are calculated. The results show that the determination of full set of vibrational levels and maximum vibrational quantum number is the key in the correct statistical analysis of bromine systematical thermodynamic property. Algebra method results are clearly different from data of simple harmonic oscillator and the related algebra method results are no longer analytical but numerical and are superior to simple harmonic oscillator results. Compared with simple harmonic oscillator’s heat capacities, the algebra method’s heat capacities are more consistent with the experimental data in the given temperature range of 600–2100 K.

Semi-strong linearity testing in linear models with dependent but uncorrelated errors

The covariance estimation of multivariate nonlinear processes is studied. The heteroscedasticity autocorrelation consistent (HAC) and White (1980) estimators are commonly used in the literature to take into account nonlinearities. Noting that the more general HAC estimation procedures may be sometimes viewed too sophisticated in applications, we propose tests for determining whether the simple White estimation could be used or if HAC estimation is necessary to ensure a correct statistical analysis of time series. The theoretical results are illustrated by mean of Monte Carlo experiments.

Research Design and Statistical Methods in Indian Medical Journals: A Retrospective Survey

Good quality medical research generally requires not only an expertise in the chosen medical field of interest but also a sound knowledge of statistical methodology. The number of medical research articles which have been published in Indian medical journals has increased quite substantially in the past decade. The aim of this study was to collate all evidence on study design quality and statistical analyses used in selected leading Indian medical journals. Ten (10) leading Indian medical journals were selected based on impact factors and all original research articles published in 2003 (N = 588) and 2013 (N = 774) were categorized and reviewed. A validated checklist on study design, statistical analyses, results presentation, and interpretation was used for review and evaluation of the articles. Main outcomes considered in the present study were – study design types and their frequencies, error/defects proportion in study design, statistical analyses, and implementation of CONSORT checklist in RCT (randomized clinical trials). From 2003 to 2013: The proportion of erroneous statistical analyses did not decrease (χ2=0.592, Φ=0.027, p=0.4418), 25% (80/320) in 2003 compared to 22.6% (111/490) in 2013. Compared with 2003, significant improvement was seen in 2013; the proportion of papers using statistical tests increased significantly (χ2=26.96, Φ=0.16, p<0.0001) from 42.5% (250/588) to 56.7 % (439/774). The overall proportion of errors in study design decreased significantly (χ2=16.783, Φ=0.12 p<0.0001), 41.3% (243/588) compared to 30.6% (237/774). In 2013, randomized clinical trials designs has remained very low (7.3%, 43/588) with majority showing some errors (41 papers, 95.3%). Majority of the published studies were retrospective in nature both in 2003 [79.1% (465/588)] and in 2013 [78.2% (605/774)]. Major decreases in error proportions were observed in both results presentation (χ2=24.477, Φ=0.17, p<0.0001), 82.2% (263/320) compared to 66.3% (325/490) and interpretation (χ2=25.616, Φ=0.173, p<0.0001), 32.5% (104/320) compared to 17.1% (84/490), though some serious ones were still present. Indian medical research seems to have made no major progress regarding using correct statistical analyses, but error/defects in study designs have decreased significantly. Randomized clinical trials are quite rarely published and have high proportion of methodological problems.

Tinnitus: a large VBM-EEG correlational study.

A surprising fact in voxel-based morphometry (VBM) studies performed in tinnitus is that not one single region is replicated in studies of different centers. The question then rises whether this is related to the low sample size of these studies, the selection of non-representative patient subgroups, or the absence of stratification according to clinical characteristics. Another possibility is that VBM is not a good tool to study functional pathologies such as tinnitus, in contrast to pathologies like Alzheimer’s disease where it is known the pathology is related to cell loss. In a large sample of 154 tinnitus patients VBM and QEEG (Quantitative Electroencephalography) was performed and evaluated by a regression analysis. Correlation analyses are performed between VBM and QEEG data. Uncorrected data demonstrated structural differences in grey matter in hippocampal and cerebellar areas related to tinnitus related distress and tinnitus duration. After control for multiple comparisons, only cerebellar VBM changes remain significantly altered. Electrophysiological differences are related to distress, tinnitus intensity, and tinnitus duration in the subgenual anterior cingulate cortex, dorsal anterior cingulate cortex, hippocampus, and parahippocampus, which confirms previous results. The absence of QEEG-VBM correlations suggest functional changes are not reflected by co-occurring structural changes in tinnitus, and the absence of VBM changes (except for the cerebellum) that survive correct statistical analysis in a large study population suggests that VBM might not be very sensitive for studying tinnitus.