CircMTA1 promotes glioblastoma angiogenesis by encoding MTA1-134aa.

Glioblastoma multiforme (GBM) is the most malignant brain tumor with rapid angiogenesis. How to inhibit GBM angiogenesis is a key problem to be solved. To explore the targets of inhibiting GBM angiogenesis, this study confirmed that the expression of circMTA1 (hsa_circ_0033614) was significantly upregulated in human brain microvascular endothelial cells exposed to glioma cell-conditioned medium (GECs). The expression of circMTA1 in the cytoplasm was significantly higher than that in the nucleus. Upregulated circMTA1 in GECs can promote cell proliferation, migration, and tube formation. Further exploration of the circularization mechanism of circMTA1 confirmed that KHDRBS1 protein can bind to the upstream and downstream flanking sequences of circMTA1 and promote circMTA1 biogenesis by coordinating Alu element pairing. KHDRBS1 upregulated the proliferation, migration, and tube formation of GECs by promoting the biogenesis of circMTA1. CircMTA1 can encode the protein MTA1-134aa by internal ribosome entry site sequence-mediated translation mechanism, and promote the proliferation, migration, and tube formation of GECs through the encoded MTA1-134aa. This study provides a new target for inhibiting angiogenesis in brain GBM and a new strategy for improving the therapeutic efficacy of GBM.

Calibration transfer between NIR instruments using optimally predictive calibration subsets.

In this study, a new approach for the selection of informative standardization samples from the original calibration set for the transfer of a calibration model between NIR instruments is proposed and evaluated. First, a calibration model is developed, after variable selection by the Final Complexity Adapted Models (FCAM) method, using the significance of the PLS regression coefficients (FCAM-SIG) as selection criterion. Then, the resulting model is used for the selection of the best fitting subset of calibration samples with optimally predictive ability, called the optimally predictive calibration subset (OPCS). Next, the standardization samples are selected from the OPCS. The spectra on the slave instruments are transferred to corresponding spectra on the master instrument by the widely used Piecewise Direct Standardization (PDS) method. Thereafter, for the test set on the slave instrument, a 3D response surface plot is drawn for the root mean squared error of prediction (RMSEP) as a function of the number of OPCS samples and window sizes used for the PDS method. Finally, the smallest set of calibration samples, in combination with the optimal window size, providing the optimal RMSEP, is selected as standardization set. The proposed OPCS approach for the selection of standardization samples is tested on two real-life NIR data sets providing 13 X-y combinations to model. The results show that the obtained numbers of OPCS-based standardization samples are statistically significantly lower than those obtained with the widely used representative sample selection method of Kennard and Stone, while the predictive performances are similar.

Characterizing the Adoption and Experiences of Users of Artificial Intelligence-Generated Health Information in the United States: Cross-Sectional Questionnaire Study.

BACKGROUND: OpenAI's ChatGPT is a source of advanced online health information (OHI) that may be integrated into individuals' health information-seeking routines. However, concerns have been raised about its factual accuracy and impact on health outcomes. To forecast implications for medical practice and public health, more information is needed on who uses the tool, how often, and for what. OBJECTIVE: This study aims to characterize the reasons for and types of ChatGPT OHI use and describe the users most likely to engage with the platform. METHODS: In this cross-sectional survey, patients received invitations to participate via the ResearchMatch platform, a nonprofit affiliate of the National Institutes of Health. A web-based survey measured demographic characteristics, use of ChatGPT and other sources of OHI, experience characterization, and resultant health behaviors. Descriptive statistics were used to summarize the data. Both 2-tailed t tests and Pearson chi-square tests were used to compare users of ChatGPT OHI to nonusers. RESULTS: Of 2406 respondents, 21.5% (n=517) respondents reported using ChatGPT for OHI. ChatGPT users were younger than nonusers (32.8 vs 39.1 years, P<.001) with lower advanced degree attainment (BA or higher; 49.9% vs 67%, P<.001) and greater use of transient health care (ED and urgent care; P<.001). ChatGPT users were more avid consumers of general non-ChatGPT OHI (percentage of weekly or greater OHI seeking frequency in past 6 months, 28.2% vs 22.8%, P<.001). Around 39.3% (n=206) respondents endorsed using the platform for OHI 2-3 times weekly or more, and most sought the tool to determine if a consultation was required (47.4%, n=245) or to explore alternative treatment (46.2%, n=239). Use characterization was favorable as many believed ChatGPT to be just as or more useful than other OHIs (87.7%, n=429) and their doctor (81%, n=407). About one-third of respondents requested a referral (35.6%, n=184) or changed medications (31%, n=160) based on the information received from ChatGPT. As many users reported skepticism regarding the ChatGPT output (67.9%, n=336), most turned to their physicians (67.5%, n=349). CONCLUSIONS: This study underscores the significant role of AI-generated OHI in shaping health-seeking behaviors and the potential evolution of patient-provider interactions. Given the proclivity of these users to enact health behavior changes based on AI-generated content, there is an opportunity for physicians to guide ChatGPT OHI users on an informed and examined use of the technology.

An Objective and Robust Bayes Factor for the Hypothesis Test One Sample and Two Population Means.

It has been over 100 years since the discovery of one of the most fundamental statistical tests: the Student's t test. However, reliable conventional and objective Bayesian procedures are still essential for routine practice. In this work, we proposed an objective and robust Bayesian approach for hypothesis testing for one-sample and two-sample mean comparisons when the assumption of equal variances holds. The newly proposed Bayes factors are based on the intrinsic and Berger robust prior. Additionally, we introduced a corrected version of the Bayesian Information Criterion (BIC), denoted BIC-TESS, which is based on the effective sample size (TESS), for comparing two population means. We studied our developed Bayes factors in several simulation experiments for hypothesis testing. Our methodologies consistently provided strong evidence in favor of the null hypothesis in the case of equal means and variances. Finally, we applied the methodology to the original Gosset sleep data, concluding strong evidence favoring the hypothesis that the average sleep hours differed between the two treatments. These methodologies exhibit finite sample consistency and demonstrate consistent qualitative behavior, proving reasonably close to each other in practice, particularly for moderate to large sample sizes.

Rank normalization empowers a t-test for microbiome differential abundance analysis while controlling for false discoveries.

A major task in the analysis of microbiome data is to identify microbes associated with differing biological conditions. Before conducting analysis, raw data must first be adjusted so that counts from different samples are comparable. A typical approach is to estimate normalization factors by which all counts in a sample are multiplied or divided. However, the inherent variation associated with estimation of normalization factors are often not accounted for in subsequent analysis, leading to a loss of precision. Rank normalization is a nonparametric alternative to the estimation of normalization factors in which each count for a microbial feature is replaced by its intrasample rank. Although rank normalization has been successfully applied to microarray analysis in the past, it has yet to be explored for microbiome data, which is characterized by high frequencies of 0s, strongly correlated features and compositionality. We propose to use rank normalization as an alternative to the estimation of normalization factors and examine its performance when paired with a two-sample t-test. On a rigorous 3rd-party benchmarking simulation, it is shown to offer strong control over the false discovery rate, and at sample sizes greater than 50 per treatment group, to offer an improvement in performance over commonly used normalization factors paired with t-tests, Wilcoxon rank-sum tests and methodologies implemented by R packages. On two real datasets, it yielded valid and reproducible results that were strongly in agreement with the original findings and the existing literature, further demonstrating its robustness and future potential. Availability: The data underlying this article are available online along with R code and supplementary materials at https://github.com/matthewlouisdavisBioStat/Rank-Normalization-Empowers-a-T-Test.

Comparison of six machine learning methods for differentiating benign and malignant thyroid nodules using ultrasonographic characteristics.

BACKGROUND: Several machine learning (ML) classifiers for thyroid nodule diagnosis have been compared in terms of their accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and area under the receiver operating curve (AUC). A total of 525 patients with thyroid nodules (malignant, n = 228; benign, n = 297) underwent conventional ultrasonography, strain elastography, and contrast-enhanced ultrasound. Six algorithms were compared: support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), logistic regression (LG), GlmNet, and K-nearest neighbors (K-NN). The diagnostic performances of the 13 suspicious sonographic features for discriminating benign and malignant thyroid nodules were assessed using different ML algorithms. To compare these algorithms, a 10-fold cross-validation paired t-test was applied to the algorithm performance differences. RESULTS: The logistic regression algorithm had better diagnostic performance than the other ML algorithms. However, it was only slightly higher than those of GlmNet, LDA, and RF. The accuracy, sensitivity, specificity, NPV, PPV, and AUC obtained by running logistic regression were 86.48%, 83.33%, 88.89%, 87.42%, 85.20%, and 92.84%, respectively. CONCLUSIONS: The experimental results indicate that GlmNet, SVM, LDA, LG, K-NN, and RF exhibit slight differences in classification performance.

N-Substituted piperidine-3-carbohydrazide-hydrazones against Alzheimer's disease: Synthesis and evaluation of cholinesterase, beta-amyloid inhibitory activity, and antioxidant capacity.

A series of piperidine-3-carbohydrazide-hydrazones bearing phenylethyl, phenylpropyl, and phenylbutyl substituents on piperidine nitrogen were designed and synthesized as cholinesterase (ChE) inhibitors. The title compounds were screened for acetylcholinesterase (AChE), butyrylcholinesterase (BuChE) inhibitory activities and antioxidant capacities, and the active ones for Aß42 self-aggregation inhibition, in vitro. The chemiluminescence method was used to determine the effect of the selected compounds on the reactive oxygen species (ROS) levels in brain tissue. Physicochemical properties were calculated by the MOE program. Kinetic analysis and molecular modeling studies were also carried out for the most active compounds. Generally, the final compounds exhibited moderate to good AChE or BuChE inhibitory activity. Among them, 3g and 3j showed the most potent activity against AChE (IC50 = 4.32 µM) and BuChE (IC50 = 1.27 µM), respectively. The kinetic results showed that both compounds exhibited mixed-type inhibition. Among the selected compounds, nitro derivatives (3g, 4g, and 5g) provided better Aß42 inhibition. According to the chemiluminescence assay, 4i exhibited the most active superoxide free-radical scavenger activity and 3g, 3j, and 4i showed similar scavenger activity on other ROS. All results suggested that 3g, 3j, and 4i have good AChE/BuChE, Aß42 inhibitory potentials and antioxidant capacities and can therefore be suggested as promising multifunctional agents to combat Alzheimer's disease.

Incorrect analysis in "Effects of the application of a food processing-based classification system in obese women: A randomized controlled pilot study" has resulted in incorrect conclusions of demonstrated effects where no such effects have been demonstrated.

In their 2023 Nutrition and Health paper "Effects of the application of a food processing-based classification system in obese women: A randomized controlled pilot study", Giacomello et al. investigated the effects of an educational intervention based on the Dietary Guidelines for the Brazilian Population among obese women. The authors concluded that the intervention significantly improved weight loss, quality of life, components of metabolic syndrome, and pain. However, we believe the statistical analysis employed in the study was flawed. The authors used within-group changes to draw conclusions, which is known as a difference in nominal significance error. This error has the potential to inflate Type I error rates substantially. To address this issue, we re-analyzed the data obtained from the authors. We focused on body mass and hip circumference and replicated the incorrectly chosen within-group analyses, which remained significant. However, to properly evaluate the intervention's effectiveness, it is essential to compare the differences between the groups directly. Therefore, we calculated change scores for each participant and used independent samples t-tests and linear mixed models to compare between-group differences. Both methods yielded similar non-significant p-values, indicating that there is no significant effect of treatment on body mass or hip circumference. The original paper's conclusions regarding the effectiveness of the intervention are not supported by the proper statistical analysis. The data should be re-analyzed using appropriate between-group comparisons, and the corrected results should be published, or the incorrect results and original paper should be retracted.

How does the COVID-19-related restriction affect the spatiotemporal variability of ambient air quality in a tropical city?

The ambient air, a significant hazard to human health in most Indian cities, including Rourkela, is something we are strangely neglecting in the age of industrialization and urbanization. High levels of particulate matter released from various anthropogenic sources over the past decade have had a significant negative impact on the city. The COVID-19 lockdown situation brings understanding and realization towards the improvement of air quality and its subsequent effects. The present study investigates the impact of the COVID-19-related lockdown on the spatiotemporal variation of the ambient air quality in Rourkela City with a tropical climatic setup. The concentration and distribution of various pollutants are well explained by the wind rose and Pearson correlation. There is considerable spatiotemporal variation in the city's ambient air quality, as determined by a two-way ANOVA test comparing sampling sites and months. During the COVID-19 lockdown phases, the air quality of Rourkela witnessed an improvement in annual AQI ranging from 12.64 to 26.85% across the city. However, the air quality in the city deteriorated by 13.76-65.79% after the revocation of COVID-19 restrictions. The paired sample T-test justified that the air quality of Rourkela was significantly healthier in 2020 compared to both 2019 and 2021. Spatial interpolation reveals that the ambient air quality of Rourkela ranged from satisfactory to moderate categories throughout the entire study period. 31.93% area of the city has experienced an improvement in AQI from the Moderate to the satisfying category from 2019 to 2020, whereas about 68.78% area of the city has witnessed a decline in AQI from satisfactory to moderate category from 2020 to 2021.

The impacts of Covid-19 pandemic on the sustainable mobility of university members in Turkey.

The global Covid-19 pandemic had a devastating impact on our lives. The pandemic led to sudden and momentous changes in mobility styles and travel habits. Many users started preferring to travel via private vehicles, which is contrary to sustainability policies. Strict measures were implemented against the Covid-19 pandemic in Turkey during this process, as was the case all over the world. Taking into consideration these realities, the aim of the present study was to examine the impacts of the Covid-19 pandemic on the travel modes (public transportation, walking, and bicycle), anxiety and stress perceptions, and working conditions of individuals. A survey was conducted to measure the impacts of the pandemic and the measures taken. The sample of this study consists of people who regularly attended Suleyman Demirel University in Isparta and Akdeniz University in Antalya, Turkey, before the pandemic. An online survey was conducted for the case study during the May-June 2021 semester, and the survey was representative of the study population with a total of 556 participants. The travel time for both different university students decreased during the pandemic period. The percentage of those traveling to the university with their own cars increased to 77 % in AU and 66.8 % in SDU. The use of public transportation dropped to 6.1 % in AU and 11.8 % in SDU. 56.97 % of AU participants and 51.15 % of SDU participants reported that their walking habits decreased. It was reported that 52.73 % of the respondents in AU and 55.75 % of the respondents in SDU did not change their bicycle usage frequency. 64.24 % of AU and 74.42 % of SDU reported that their anxiety levels increased during the pandemic period. And for each of these analyses, there was no statistical difference between the two different university students.

Exploring the convergence patterns of PM2.5 in Chinese cities.

Economic development and ongoing urbanization are usually accompanied by severe haze pollution. Revealing the spatial and temporal evolution of haze pollution can provide a powerful tool for formulating sustainable development policies. Previous studies mostly discuss the differences in the level of PM2.5 among regions, but have paid little attention to the change rules of such differences and their clustering patterns over long periods. Therefore, from the perspective of club convergence, this study employs the log t regression test and club clustering algorithm proposed by Phillips and Sul (Econometrica 75(6):1771-1855, 2007. 10.1111/j.1468-0262.2007.00811.x) to empirically examine the convergence characteristics of PM2.5 concentrations in Chinese cities from 1998 to 2016. This study found that there was no evidence of full panel convergence, but supported one divergent group and eleven convergence clubs with large differences in mean PM2.5 concentrations and growth rates. The geographical distribution of these clubs showed significant spatial dependence. In addition, certain meteorological and socio-economic factors predominantly determined the convergence club for each city.

A closed max-t test for multiple comparisons of areas under the ROC curve.

Comparing areas under the ROC curve (AUCs) is a popular approach to compare prognostic biomarkers. The aim of this paper is to present an efficient method to control the family-wise error rate when multiple comparisons are performed. We suggest to combine the max-t test and the closed testing procedures. We build on previous work on asymptotic results for ROC curves and on general multiple testing methods to efficiently take into account both the correlations between the test statistics and the logical constraints between the null hypotheses. The proposed method results in an uniformly more powerful procedure than both the single-step max-t test procedure and popular stepwise extensions of the Bonferroni procedure, such as Bonferroni-Holm. As demonstrated in this paper, the method can be applied in most usual contexts, including the time-dependent context with right censored data. We show how the method works in practice through a motivating example where we compare several psychometric scores to predict the t-year risk of Alzheimer's disease. The example illustrates several multiple testing settings and demonstrates the advantage of using the proposed methods over common alternatives. R code has been made available to facilitate the use of the methods by others.

Distinguishing between Decaffeinated and Regular Coffee by HS-SPME-GC×GC-TOFMS, Chemometrics, and Machine Learning.

Coffee, one of the most popular beverages in the world, attracts consumers by its rich aroma and the stimulating effect of caffeine. Increasing consumers prefer decaffeinated coffee to regular coffee due to health concerns. There are some main decaffeination methods commonly used by commercial coffee producers for decades. However, a certain amount of the aroma precursors can be removed together with caffeine, which could cause a thin taste of decaffeinated coffee. To understand the difference between regular and decaffeinated coffee from the volatile composition point of view, headspace solid-phase microextraction two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS) was employed to examine the headspace volatiles of eight pairs of regular and decaffeinated coffees in this study. Using the key aroma-related volatiles, decaffeinated coffee was significantly separated from regular coffee by principal component analysis (PCA). Using feature-selection tools (univariate analysis: t-test and multivariate analysis: partial least squares-discriminant analysis (PLS-DA)), a group of pyrazines was observed to be significantly different between regular coffee and decaffeinated coffee. Pyrazines were more enriched in the regular coffee, which was due to the reduction of sucrose during the decaffeination process. The reduction of pyrazines led to a lack of nutty, roasted, chocolate, earthy, and musty aroma in the decaffeinated coffee. For the non-targeted analysis, the random forest (RF) classification algorithm was used to select the most important features that could enable a distinct classification between the two coffee types. In total, 20 discriminatory features were identified. The results suggested that pyrazine-derived compounds were a strong marker for the regular coffee group whereas furan-derived compounds were a strong marker for the decaffeinated coffee samples.

Empirical analysis on the impact of the COVID-19 pandemic on food insecurity in South Africa.

The study sought to ascertain the changes in the food insecurity status of households during the COVID-19 pandemic. The study made use of secondary data obtained from the 5 Waves of the National Income Dynamics Study-Coronavirus Rapid Mobile Survey (NIDS-CRAM). Descriptive statistics, food insecurity index and independent sample t-test were used to compare the mean differences in the food insecurity statuses of the households over the 5 Waves. The study found that there was an increase in food insecurity as the COVID-19 progressed from Wave 1 to 5. Significant differences at the 1% level were observed between Wave 5 and Wave 1 as well as between Wave 5 and Wave 3. The study concludes that there was food security in the initial progression of the COVID-19 pandemic which deteriorated. The study recommends a reconsideration of the scrapping of the top ups on the social grants. This will likely tighten the dire economic situation the households find themselves in. There is need to expand the social safety nets to accommodate the vulnerable in society. Short and localised value chains should be promoted to improve food accessibility during times of crisis.

Comparing the epidemiological situation of selected sexually transmitted infection in three Czech regions between 2006 and 2013

Aim: The research aimed to identify and describe the main differences in the incidence of sexually transmitted infections in three regions of the Czech Republic. Methods: Descriptive cross-section study analyses data received from each institution that allowed access to the Registry of sexually transmitted illnesses of individual regions. Mainly three regions are being studied, yet the study considers only the data from the same time-period in all three regions (i. e., 2006­2013). To compare the development of the epidemiological situation, we considered the incidence rate (IR) and specific incidence rate for both age and age groups. Methods of descriptive statistics were implemented. For the analysis of statistically significant differences, we used ANOVA when p = 0.01, alternatively in some cases F-test before an unpaired T-test to test variability. Results: The highest incidence rate for all integrated STIs was reported in the Capital City of Prague region (CCP) ­ 398.3 cases per 100 thousand ­ whereas gonorrhoea appeared the most in CCP and Central Bohemia regions (CBR) and syphilis occurred the most in the Pilsen region (PLS). ANOVA identified statistically significant differences between all integrated regions within the frame of incidence of all STIs as a group of infections, and also as individual STIs. Of course, the T-test identified statistical differences in most cases only between CCP and the other two regions, even though these differences weren't identified between CBR and PLR. Likewise, there were statistically important differences identified (by ANOVA for all three regions mutually) for the specific incidence rate for gender and the 15­64 age group category where the most cases were aggregated. In all three regions, there were more male cases (CCP 75%, PLR 58,7% and CBR 66%) with most of them being composed of Czechs (69.7­79.8 %). HIV coinfection was reported in 5.5% of cases in CCP, 3.9% in PLR and 5.3% in CBR. Conclusion: This study submits the results of data comparison from three Czech regions in the 2006­2013 time-period. It points out differences in the development of sexually transmitted infections in individual regions, mainly in men having sex with men and other risk groups. It also invokes the issue of the insufficient level of knowledge concerning sexually transmitted infections in the MSM subpopulation.

Emotional intelligence and burnout among medical officers of twin cities in Pakistan.

OBJECTIVE: To compare the level of burnout and emotional intelligence among junior and senior medical officers. METHODS: The cross-sectional observational study was conducted at the Department of Applied Psychology, Government College Women University, Faisalabad, Pakistan, from February to December 2017, and comprised medical officers of either gender aged 25-45 years with professional experience 1-10 years enrolled from different government and private hospitals of Faisalabad and Lahore. Data was collected using Schutte Emotional intelligence Test and Professional Quality of Life scale. Data wsa analysed using SPSS 21. RESULTS: Of the 160 subjects, 90(56.3%) were male and 70(43.8%) were female medical officers. The overall mean age was 29.39±4.65 years, Further, 95(59.4%) subjects had professional experience 1-2 years, while 65(40.6%) had 3-10 years. Junior medical officers experienced significantly higher level of burnout and emotional intelligence compared to the seniors (p<0.05). CONCLUSIONS: Junior medical officers were found to have significantly higher level of burnout and emotional intelligence compared to the seniors.

Predicting COVID-19 statistics using machine learning regression model: Li-MuLi-Poly.

In this paper, linear regression (LR), multi-linear regression (MLR) and polynomial regression (PR) techniques are applied to propose a model Li-MuLi-Poly. The model predicts COVID-19 deaths happening in the United States of America. The experiment was carried out on machine learning model, minimum mean square error model, and maximum likelihood ratio model. The best-fitting model was selected according to the measures of mean square error, adjusted mean square error, mean square error, root mean square error (RMSE) and maximum likelihood ratio, and the statistical t-test was used to verify the results. Data sets are analyzed, cleaned up and debated before being applied to the proposed regression model. The correlation of the selected independent parameters was determined by the heat map and the Carl Pearson correlation matrix. It was found that the accuracy of the LR model best-fits the dataset when all the independent parameters are used in modeling, however, RMSE and mean absolute error (MAE) are high as compared to PR models. The PR models of a high degree are required to best-fit the dataset when not much independent parameter is considered in modeling. However, the PR models of low degree best-fits the dataset when independent parameters from all dimensions are considered in modeling.

Influence of upper-limb muscle strength on the repeated change of direction ability in international-level wheelchair basketball players.

This study aims 1) to compare repeated change of direction ability (rCODA) and bench press (BP) between low (A category, ≤2.5) and high (B category, ≥3.0) sport classes and 2) to analyse the relationships between rCODA and BP performance in a sample of wheelchair basketball (WB) players. Seventeen world-class WB players volunteered participated in this study. All the players undertook two tests: the repeated (x12) Modified Agility T-test (rMAT) to measure the rCODA and the movement velocity in a BP test. No significant differences were observed between categories in the rMAT and BP. For the total sample, BP variables with a mean propulsive velocity of 1 m·s-1 (V1LOAD) largely correlated with all the full rMAT outcomes (r>-0.625; p< 0.05). Having a better BP could be favourable to perform repeated efforts and this seems particularly relevant in fatigue conditions, and especially for the B category players.

Moderated t-tests for group-level fMRI analysis.

In recent years, there has been significant criticism of functional magnetic resonance imaging (fMRI) studies with small sample sizes. The argument is that such studies have low statistical power, as well as reduced likelihood for statistically significant results to be true effects. The prevalence of these studies has led to a situation where a large number of published results are not replicable and likely false. Despite this growing body of evidence, small sample fMRI studies continue to be regularly performed; likely due to the high cost of scanning. In this report we investigate the use of a moderated t-statistic for performing group-level fMRI analysis to help alleviate problems related to small sample sizes. The proposed approach, implemented in the popular R-package LIMMA (linear models for microarray data), has found wide usage in the genomics literature for dealing with similar issues. Utilizing task-based fMRI data from the Human Connectome Project (HCP), we compare the performance of the moderated t-statistic with the standard t-statistic, as well as the pseudo t-statistic commonly used in non-parametric fMRI analysis. We find that the moderated t-test significantly outperforms both alternative approaches for studies with sample sizes less than 40 subjects. Further, we find that the results were consistent both when using voxel-based and cluster-based thresholding. We also introduce an R-package, LIMMI (linear models for medical images), that provides a quick and convenient way to apply the method to fMRI data.

Bayesian Hodges-Lehmann tests for statistical equivalence in the two-sample setting: Power analysis, type I error rates and equivalence boundary selection in biomedical research.

BACKGROUND: Null hypothesis significance testing (NHST) is among the most frequently employed methods in the biomedical sciences. However, the problems of NHST and p-values have been discussed widely and various Bayesian alternatives have been proposed. Some proposals focus on equivalence testing, which aims at testing an interval hypothesis instead of a precise hypothesis. An interval hypothesis includes a small range of parameter values instead of a single null value and the idea goes back to Hodges and Lehmann. As researchers can always expect to observe some (although often negligibly small) effect size, interval hypotheses are more realistic for biomedical research. However, the selection of an equivalence region (the interval boundaries) often seems arbitrary and several Bayesian approaches to equivalence testing coexist. METHODS: A new proposal is made how to determine the equivalence region for Bayesian equivalence tests based on objective criteria like type I error rate and power. Existing approaches to Bayesian equivalence testing in the two-sample setting are discussed with a focus on the Bayes factor and the region of practical equivalence (ROPE). A simulation study derives the necessary results to make use of the new method in the two-sample setting, which is among the most frequently carried out procedures in biomedical research. RESULTS: Bayesian Hodges-Lehmann tests for statistical equivalence differ in their sensitivity to the prior modeling, power, and the associated type I error rates. The relationship between type I error rates, power and sample sizes for existing Bayesian equivalence tests is identified in the two-sample setting. Results allow to determine the equivalence region based on the new method by incorporating such objective criteria. Importantly, results show that not only can prior selection influence the type I error rate and power, but the relationship is even reverse for the Bayes factor and ROPE based equivalence tests. CONCLUSION: Based on the results, researchers can select between the existing Bayesian Hodges-Lehmann tests for statistical equivalence and determine the equivalence region based on objective criteria, thus improving the reproducibility of biomedical research.