Search

Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects.

Current interventions for oral/dental diseases heavily rely on operative/surgical procedures, while the discovery of novel drug targets may enable access to noninvasive pharmacotherapy. Therefore, this study aims to leverage large-scale data and Mendelian randomization (MR) techniques, utilizing genetic variants as instruments, to identify potential therapeutic targets for oral and dental diseases supported by genetic evidence. By intersecting 4,302 druggable genes with expression quantitative trait loci from 31,684 blood samples, we identified 2,580 druggable targets as exposures. Single nucleotide polymorphisms associated with dental disease/symptom traits were collected from FinnGen R9, the Gene–Lifestyle Interactions in Dental Endpoints consortium, and the UK Biobank to serve as outcomes for both discovery and replication purposes. Through MR analysis, we identified 43 druggable targets for various dental disease/symptom traits. To evaluate the viability of these targets, we replicated the analysis using circulating protein quantitative trait loci as exposures. Additionally, we conducted sensitivity, colocalization, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotation, protein–protein interaction analyses, and validated dental trait–associated druggable gene expression in animal models. Among these targets, IL12RB1 (odds ratio [OR], 1.01; 95% confidence interval [CI], 1.01–1.01) and TNF (OR, 0.98; 95% CI, 0.97–0.99) exhibited therapeutic promise for oral ulcers, whereas CXCL10 (OR, 0.84; 95% CI, 0.76–0.91) was for periodontitis. Through a rigorous quality control and validation pipeline, our study yields compelling evidence for these druggable targets, which may enhance the clinical prognosis by developing novel drugs or repurposing existing ones. [ABSTRACT FROM AUTHOR]

Weeds are a major concern in crop production, and their environmental plasticity hinders successful control. A phytosociological study assessed weed distribution patterns in smallholder maize (Zea mays L.) production under different climatic and soil factors in the 2020/2021 and 2021/2022 cropping seasons. Thirty‐six maize fields from the Highveld (Mankayane) and the Middleveld (Luve) of Eswatini, were investigated with three fertiliser regimes applied (cattle manure only, cattle manure plus inorganic fertiliser and inorganic fertiliser only). Phytosociological attributes of weed species including density were collected using a 25 m transect placed horizontally at the centre of each field. Climatic factors and soil physiochemical properties considered included altitude, rainfall, temperature, soil texture, soil pH, nitrogen, phosphorus, potassium, calcium, magnesium, copper, iron, zinc and manganese. Fifty‐six weed species belonging to 16 families were recorded. Distance‐based multivariate multiple regression (DistLM) analysis was used to determine the relationship between weed species composition as the dependent variable from the fertiliser regimes and the environmental factors as the independent variable. Eight environmental variables explained 25% variation in species distribution namely; altitude, temperature, nitrogen, phosphorus, potassium, copper, magnesium and percentage silt. At Luve, temperature, copper and magnesium significantly explained weed distribution in all three fertiliser regimes, while phosphorus influenced weed distribution in the manure only regime. At Mankayane, nitrogen affected weed distribution in the manure plus inorganic fertiliser and inorganic fertiliser regimes whereas altitude, phosphorus, potassium, and %silt influenced weed distribution in the manure only and manure plus inorganic fertiliser regimes. The observations suggest that some weed species coexist between fertiliser regimes and study areas while others occur in specific environments only. [ABSTRACT FROM AUTHOR]

Background: The selection of statistical analysis methods in research is a critical and nuanced task that requires a scientific and rational approach. Aligning the chosen method with the specifics of the research design and hypothesis is paramount, as it can significantly impact the reliability and quality of the research outcomes. Methods: This study explores a comprehensive guideline for systematically choosing appropriate statistical analysis methods, with a particular focus on the statistical hypothesis testing stage and categorization of variables. By providing a detailed examination of these aspects, this study aims to provide researchers with a solid foundation for informed methodological decision making. Moving beyond theoretical considerations, this study delves into the practical realm by examining the null and alternative hypotheses tailored to specific statistical methods of analysis. The dynamic relationship between these hypotheses and statistical methods is thoroughly explored, and a carefully crafted flowchart for selecting the statistical analysis method is proposed. Results: Based on the flowchart, we examined whether exemplary research papers appropriately used statistical methods that align with the variables chosen and hypotheses built for the research. This iterative process ensures the adaptability and relevance of this flowchart across diverse research contexts, contributing to both theoretical insights and tangible tools for methodological decision-making. Conclusions: This study emphasizes the importance of a scientific and rational approach for the selection of statistical analysis methods. By providing comprehensive guidelines, insights into the null and alternative hypotheses, and a practical flowchart, this study aims to empower researchers and enhance the overall quality and reliability of scientific studies. [ABSTRACT FROM AUTHOR]

Epidemiology is experiencing a significant shift toward the utilization of big data for health monitoring and decision-making. This article discusses the recent example of the World Health Organization (WHO) global oral health status report and regional summaries, which faced criticisms due to its reliance on big data from the Global Burden of Disease (GBD) study. We address the arguments for and against the use of big data in epidemiology and provide an assessment of the value and limitations of big data epidemiology. Moreover, we provide recommendations as to how the oral health community should reconcile traditional epidemiologic approaches with big data and advanced data analytics. This Perspective article highlights the challenges of the current epidemiologic landscape, the potential of big data, and the need for a balanced approach to data utilization in epidemiology. [ABSTRACT FROM AUTHOR]

Researchers and practitioners are typically familiar with descriptive statistics and statistical inference. However, outside of regression techniques, little attention may be given to questions around prediction. In the current paper, we introduce prediction intervals using fundamental concepts that are learned in descriptive and inferential statistical training (i.e., sampling error, standard deviation). We walk through an example using simple hand calculations and reference a simple R package that can be used to calculate prediction intervals. [ABSTRACT FROM AUTHOR]

The delivery of academic statistical collaboration resources can vary among types of institutions and across time. In particular, this variation might occur in the management of infrastructure and the business model, the staffing model and opportunities for staff development. In this manuscript, we present examples of these three themes in modern academic statistical collaboration units and describe key advantages and challenges. [ABSTRACT FROM AUTHOR]

Members of the ASA's Section on Statistical Consulting established the Pathways to Promotion Committee in 2021 to provide guidance and support for navigating a career as a collaborative statistician. In three years of existence, the Committee has produced seven webinars on relevant topics, each attended by more than one hundred participants. Committee members have given four oral presentations, organized three roundtables, led multiple discussions at ASA meetings and published four articles. These efforts have inspired, created and facilitated new connections for collaborative statisticians who feel isolated in their career path. This paper describes the formation and development of the Committee, reports its impact on the community of collaborative statisticians and discusses potential future directions. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the impact of different components of daily training load during the week preceding the match on the risk of sustaining a match injury in professional rugby union. A cohort of 72 players from a single professional French club participated. Global positioning system-derived data including total distance (TD) and high-speed distance in addition to ratings of perceived effort (RPE) for both on- and off-pitch (gym-based strength conditioning work) training were collected for each training session over 3 seasons (2017-2020). The association between the daily measures of external and internal training load over the week preceding the day of the match (MD) and the subsequent risk of injury in match-play was estimated using a mixed-effects logistic model adjusted for contextual and individual factors. A total of 184 injuries were sustained in 128 matches (incidence: 81.2 injuries per 1,000 player hours). Higher RPE values for the strength conditioning session on MD-5 (p < 0.001) and for the on-pitch session on MD-1 (p = 0.04) were associated with an increased risk of injury in matches. On MD-2, a higher TD covered and that run at high speed (>MAS) were, respectively, associated with a higher (p = 0.03) and lower risk (p = 0.02) of injury in matches played. This study in professional rugby union shows that different components of external and internal load had varying influences on injury risk and particularly in relation to the day on which these were performed in the week leading up to the next match. At MD-2, training load favoring intensity rather than volume could reduce the risk of match-play injury. [ABSTRACT FROM AUTHOR]

Reliance on null hypothesis significance testing often leads to misinterpretation of research results. Common misinterpretations include that a statistically nonsignificant difference (p ≥ 0.05) implies no difference between groups, and that a statistically significant finding (p < 0.05) is unbiased and clinically important. We aimed to develop a tool — the Conclusion Generator — to mitigate these misconceptions. We reviewed the content of the Conclusion Generator and validated its output using published and simulated data. The Conclusion Generator is a free online application designed to generate conclusions for scientific papers based on the values and clinical interpretation of the point estimate and confidence interval. Both relative and absolute measures of effect are supported. It offers two modes for interpretation: (1) Statistical mode provides an accurate statistical interpretation of results, with an optional specification of superiority and noninferiority bounds; (2) Clinical mode evaluates the clinical importance of the point estimate and confidence limits as specified by the user. Both modes assume no uncontrolled biases. Users must specify the number of decimals, the direction of a beneficial effect (e.g. , relative risk <1 vs. >1), and the level of detail (concise vs. elaborated) for the output. The validation confirmed the Conclusion Generator's capability to interpret research results, considering random error and clinical relevance, while avoiding common misinterpretations associated with null hypothesis significance testing. The Conclusion Generator facilitates an appropriate interpretation of research results by emphasizing estimation and clinical relevance over hypothesis testing. [ABSTRACT FROM AUTHOR]

Psychosocial properties of oral health have been reported. The present study aimed to investigate the causal effect of complete loss of natural teeth on loneliness by using fixed-effects analysis to control for confounding factors, including unmeasured time-invariant factors. Data from older adults participating in at least 2 consecutive waves of the English Longitudinal Study of Ageing in waves 3 (2006/2007), 5 (2010/2011), and 7 (2014/2015) were analyzed (N = 18,682 observations from 7,298 individuals). The association between complete loss of natural teeth and loneliness score (ranging from 3 to 9) was examined using fixed-effect linear regression analysis adjusting for time-varying confounders, including sociodemographic and health characteristics. The prevalence of complete tooth loss was 12.7%, 12.8%, and 10.6% in waves 3, 5, and 7, respectively. Individuals who transitioned to complete tooth loss during any 2 consecutive waves had an increase in loneliness score by 0.27 (95% confidence interval [CI] 0.03, 0.52), which was greater than those who maintained natural teeth (−0.03; 95% CI −0.05, −0.01). Fixed-effects analysis adjusting for time-varying confounders revealed a significant association between complete loss of natural teeth and an increase in loneliness score by 0.31 (95% CI 0.17, 0.46). Complete loss of natural teeth among older adults in England was associated with loneliness, even after accounting for measured time-varying and (un)measured time-invariant confounders. Retaining natural teeth may reduce the risk of loneliness. [ABSTRACT FROM AUTHOR]

In a biostatistics course on clinical trials, one important topic is human research ethics. In this study, an E-Learning module (e-module) was developed to train students in human research ethics. This interactive e-module provides an overview of the ethical perspectives of conducting clinical research, historical events, Nuremberg Code, three principles of the Belmont Report, and eight ethical requirements to conduct clinical research. Students in a clinical trials course were randomized to the e-module or traditional online lecture and then took a knowledge-based quiz and an optional satisfaction survey. Students indicated that they preferred learning from an e-module and would like additional e-modules added to the course. We found students randomized to the e-module did not perform as well on the knowledge-based quiz compared to those assigned to the traditional online format, however, e-module students were not given a copy of the slides. When the study was repeated in a second cohort, and both groups were given a copy of the slides, the two groups performed similarly on the knowledge-based quiz. This indicates that additional resources, such as the slides, are helpful when implementing an e-module as part of a biostatistics course.

Network analysis is a graphical statistical technique that allows rapid visualization and interpretation of associations between multiple variables. There are still few theoretical studies on this method, especially in the areas of geriatrics and gerontology research, which cover the study of different social, clinical, or physical and mental health variables. The objectives of this study were to present the main theoretical aspects of network analysis and demonstrate its applicability by analyzing studies that used this technique, offering an accessible language for all levels of knowledge in statistics. The main characteristics of the graphs, basic theoretical concepts, and scientific articles that used networks were demonstrated. This methodological study can help the reader to understand this analytical method, which is still little explored in national research. There is a scarcity of research on this subject in the areas of geriatrics and gerontology; however, technological advances, the availability of statistical programs with new data analysis resources, and the dissemination of information are relevant factors for the expansion of knowledge and the use of network analysis in this context.

Abstract Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects.

Objective: Utility values enable relative comparisons across various health conditions, providing information for efficient allocation of healthcare resources. This study aimed to (1) quantify the utility values attributable to oral health‐related quality of life (OHRQoL) in Japanese adults and (2) develop models for converting the 14‐item Oral Health Impact Profile (OHIP‐14) scores into EuroQoL 5‐dimension 5‐level (EQ‐5D‐5L)‐based utility values. Methods: This was a cross‐sectional study. Data from a large‐scale Internet survey of Japanese adults conducted in 2022 (n = 28 405; mean age 48.2 years) were analysed. Multiple linear regression models were fitted to investigate the association between OHIP‐14 scores and EQ‐5D‐5L‐based utility values, adjusting for confounders. Conversion models were developed using a random half of the participants, and the observed and predicted utility values in the other half were compared to evaluate the model performance. Results: Among the participants, 55.2% scored 0, 20.9% scored 1–5 and 23.9% scored 6–56 on the OHIP‐14, corresponding mean utility values of 0.93, 0.90 and 0.84, respectively. A one‐point increase in the OHIP‐14 score was associated with a lower utility value (coefficient: −0.0053; 95% confidence interval:health‐related quality of life −0.0056, −0.0051). The estimated utility value attributable to OHIP‐14 was −23.3 per 1000 individuals, greater than that for other prevalent chronic conditions, including hypertension and diabetes (−2.9 and −7.1 per 1000 individuals, respectively). The conversion model incorporated the OHIP‐14 total score, age, sex and self‐rated health, predicted utility scores on average and captured differences according to the number of teeth lost. However, there was a discrepancy between predicted and observed utility values in the lower utility value groups. Conclusion: OHRQoL substantially impacted utility values at the population level. The OHIP‐14 holds the potential as a valuable tool for predicting average utility values based on oral health conditions; however, the prediction performance was relatively low for individuals with a lower health‐related quality of life. [ABSTRACT FROM AUTHOR]

Data science consulting and collaboration units (DSUs) are core infrastructure for research at universities. Activities span data management, study design, data analysis, data visualization, predictive modelling, preparing reports, manuscript writing and advising on statistical methods and may include an experiential or teaching component. Partnerships are needed for a thriving DSU as an active part of the larger university network. Guidance for identifying, developing and managing successful partnerships for DSUs can be summarized in six rules: (1) align with institutional strategic plans, (2) cultivate partnerships that fit your mission, (3) ensure sustainability and prepare for growth, (4) define clear expectations in a partnership agreement, (5) communicate and (6) expect the unexpected. While these rules are not exhaustive, they are derived from experiences in a diverse set of DSUs, which vary by administrative home, mission, staffing and funding model. As examples in this paper illustrate, these rules can be adapted to different organizational models for DSUs. Clear expectations in partnership agreements are essential for high quality and consistent collaborations and address core activities, duration, staffing, cost and evaluation. A DSU is an organizational asset that should involve thoughtful investment if the institution is to gain real value. [ABSTRACT FROM AUTHOR]

This article summarises efforts towards understanding the calculation of sample size determination in Randomised controlled Clinical Trials (RCTs). Readers would gain insight into the procedures behind sample size calculation in RCTs and its significance. Additionally, researchers, including residents, will learn to calculate sample sizes independently, aiding in their postgraduate thesis or dissertation work, especially for straightforward projects. The determination of sample size depends on various factors, several of which are discussed in this article. The concepts of biostatistics, which underpin sample size calculations and other statistical computations, are also elucidated. Examples are provided to illustrate how formula variations correspond to changes in influencing factors. Furthermore, a list of relevant software tools is included.

A strong foundation of statistics is essential for undergraduates pursuing life science careers, but this foundation is frequently undermined by negative student attitudes toward statistics. This study explored life science students’ motivational attitudes toward statistics, and their relationship to performance, using expectancy-value theory as a guiding framework. According to expectancy-value theory, task-values—interest in a task (intrinsic value), importance of doing well on a task (attainment value), perceptions of the usefulness of a task (utility value), and negative aspects of engaging in a task (cost)—impact achievement outcomes. However, task-values may be better represented by differentiating each into more specific underlying dimensions, or facets. Using 360 undergraduate life science students enrolled in biostatistics courses across two institutions, this study assessed the fit of a task-value facets model for statistics and examined which task-value facets relate to performance on a statistics assessment. Results show that life science students distinguish among facets of attainment value (importance of achievement, personal importance), utility value (school, daily life, social, career/job), and cost (effort, emotional, opportunity) for statistics. Students’ perceptions of the emotional cost of statistics and the importance of achievement in statistics related to their statistics performance. Implications for teaching and research are discussed. Supplementary materials for this article are available online.

Background: As per the revised competency-based medical education guidelines given by the National medical council of India, students are offered elective posting after the first part of 3rd phase of the medical professional course. This has been started from the year 2023. There will be various postings offered and at end of the posting they will be assessed on various domains. Aims and Objectives: Our study aims to assess the impact of elective posting on the research skills and students’ perception toward it. It also aims to identify the best methods to implement elective postings. Materials and Methods: In the year 2023, using a complete enumeration technique 86 students from the third phase of MBBS at a medical college in Maharashtra, India, were registered for the 2 months of elective and would be questioned twice following consent using a pre-validated questionnaire. The first interview will take place following the completion of the first block of elective posting, and the second interview will take place following the conclusion of the second block of elective posting. Results: We observed a statistically significant difference when two blocks were compared on the domains of efforts. Students have reported higher efforts toward clinical and surgical postings. We also observed that students reported a statistically significant level of frustration in block two. In our study, we found that research work was taken more during the second block as students were sensitized in the first block regarding research. Conclusion: In our study, we have observed that research activities are taken up and processed for presentation using biostatistics in the second block of elective if resource material and support are provided in the block one. Students develop a better understanding of research with proper training.

Abstract Background Despite the numerous advantages of mastering biostatistics, medical students generally perceive biostatistics as a difficult and challenging subject and even experience anxiety during the courses. Evidence for the correlation between students’ academic achievements and their attitudes, indicating that attitudes at the beginning of the biostatistics course may affect cognitive competence at the end of the course and subsequently influence student academic performance. However, there are current disagreements regarding the measurement and evaluation of attitudes related to statistics. Thus, there is a need for standard instruments to assess them. This study was conducted to develop a Chinese version of the Survey of Attitudes Toward Statistics (SATS-36) in order to acquire a valid instrument to measure medical students’ attitudes toward biostatistics under Chinese medical educational background. Methods The Chinese version SATS-36 was developed through translation and back-translation of the original scale, with subsequent revisions based on expert advice to ensure the most appropriate item content. The local adaption was performed with a cohort of 1709 Chinese-speaking medical undergraduate and graduate students enrolled in biostatistics courses. And then, the reliability, validity and discrimination of the questionnaires were evaluated through correlation coefficient calculation, factor analysis, parallel analysis and other methods. Results The Chinese version SATS-36 consisted of 36 items and loaded a five-factor structure by factor analysis, which offered an alternative similar but not equal to that original six-factor structure. The cumulative variance contribution rate was 62.20%, the Cronbach’s α coefficient was 0.908, the Guttman split-half reliability coefficient was 0.905 and the test–retest reliability coefficient was 0.752. Discriminant analysis revealed small to large significant differences in the five attitude subscales. Conclusions The Chinese version SATS-36 with good validity and reliability in this study can be used to evaluate the learning framework of Chinese medical students.

Introduction: Treating head and neck squamous cell carcinomas (HNSCC), especially human papillomavirus negative (HPV-) and locally advanced cases, remains difficult. Our previous analyses of radiotherapy-only HNSCC clinical trials data using mechanistically-motivated models of tumor repopulation and killing by radiotherapy predicted that hyperfractionation with twice-daily fractions, or hypofractionation involving increased doses/fraction and reduced treatment durations, both improve tumor control and reduce late normal tissue toxicity, compared with standard protocols using 35x2 Gy. Here we further investigated the validity of these conclusions by analyzing a large modern dataset on 3,346 HNSCC radiotherapy patients from the University Health Network in Toronto, Canada, where 42.5% of patients were also treated with chemotherapy. Methods: We used a two-step approach that combines mechanistic modeling concepts with state-of-the-art machine learning, beginning with Random Survival Forests (RSF) for an exploratory analysis and followed by Causal Survival Forests (CSF) for a focused causal analysis. The mechanistic concept of biologically effective dose (BED) was implemented for the standard dose-independent (DI) tumor repopulation model, our alternative dose-dependent (DD) repopulation model, and a simple model with no repopulation (BEDsimp). These BED variants were included in the RSF model, along with age, stage, HPV status and other relevant variables, to predict patient overall survival (OS) and cause-specific mortality (deaths from the index cancer, other cancers or other causes). Results: Model interpretation using Shapley Additive Explanations (SHAP) values and correlation matrices showed that high values of BEDDD or BEDDI, but not BEDsimp, were associated with decreased patient mortality. Targeted causal inference analyses were then performed using CSF to estimate the causal effect of each BED variant on OS. They revealed that high BEDDD (>61.8 Gy) or BEDDI (>57.6 Gy), but not BEDsimp, increased patient restricted mean survival time (RMST) by 0.5-1.0 years and increased survival probability (SP) by 5-15% several years after treatment. In addition to population-level averages, CSF generated individual-level causal effect estimates for each patient, facilitating personalized medicine. Discussion: These findings are generally consistent with those of our previous mechanistic modeling, implying the potential benefits of altered radiotherapy fractionation schemes (e.g. 25x2.4 Gy, 20x2.75 Gy, 18x3.0 Gy) which increase BEDDD and BEDDI and counteract tumor repopulation more effectively than standard fractionation. Such regimens may represent potentially useful hypofractionated options for treating HNSCC. [ABSTRACT FROM AUTHOR]

Micronucleus detection technology is widely used in environment monitoring and food safety inspecting all over the world and it is also a routine method for undergraduates to perform scientific research thinking training in genetics experiment teaching of university. In this study, a proposal was presented to enhance the test efficiency of micronucleus detection technology. Compared with the traditional micronucleus detection method, the new method is easier to find the difference among the experimental data and therefore identify the disadvantageous effectors on biological development. This work has a certain guiding significance for teachers and students to carry out the research of laboratory opening project and identification of genetic safety of environmental factors. [ABSTRACT FROM AUTHOR]

This article summarises efforts towards understanding the calculation of sample size determination in Randomised controlled Clinical Trials (RCTs). Readers would gain insight into the procedures behind sample size calculation in RCTs and its significance. Additionally, researchers, including residents, will learn to calculate sample sizes independently, aiding in their postgraduate thesis or dissertation work, especially for straightforward projects. The determination of sample size depends on various factors, several of which are discussed in this article. The concepts of biostatistics, which underpin sample size calculations and other statistical computations, are also elucidated. Examples are provided to illustrate how formula variations correspond to changes in influencing factors. Furthermore, a list of relevant software tools is included. [ABSTRACT FROM AUTHOR]

Propensity score methods are popular to control for confounding in observational biomedical studies of risk factors or medical treatments. This paper focused on aspects of propensity score methods that often remain undiscussed, including unmeasured confounding, missing data, variable selection, statistical efficiency, estimands, the positivity assumption, and predictive performance of the propensity score model. [ABSTRACT FROM AUTHOR]

Sophisticated mathematical and computational tools have become widespread and important in veterinary pharmacology. Although the theoretical basis and practical applications of these have been widely explored in the literature, statistical inference in the context of these models has received less attention. Optimization methods, often with frequentist statistical inference, have been predominant. In contrast, Bayesian statistics have not been widely applied, but offer both practical utility and arguably greater interpretability. Veterinary pharmacology applications are generally well supported by relevant prior information, from either existing substantive knowledge, or an understanding of study and model design. This facilitates practical implementation of Bayesian analyses that can take advantage of this knowledge. This essay will explore the specification of Bayesian models relevant to veterinary pharmacology, including demonstration of prior selection, and illustrate the capability of these models to generate practically useful statistics, including uncertainty statements, that are difficult or impossible to obtain otherwise. Case studies using simulated data will describe applications in clinical trials, pharmacodynamics, and pharmacokinetics, all including multilevel modeling. This content may serve as a suitable starting point for researchers in veterinary pharmacology and related disciplines considering Bayesian estimation for their applied work. [ABSTRACT FROM AUTHOR]

Background: As per the revised competency-based medical education guidelines given by the National medical council of India, students are offered elective posting after the first part of 3rd phase of the medical professional course. This has been started from the year 2023. There will be various postings offered and at end of the posting they will be assessed on various domains. Aims and Objectives: Our study aims to assess the impact of elective posting on the research skills and students’ perception toward it. It also aims to identify the best methods to implement elective postings. Materials and Methods: In the year 2023, using a complete enumeration technique 86 students from the third phase of MBBS at a medical college in Maharashtra, India, were registered for the 2 months of elective and would be questioned twice following consent using a pre-validated questionnaire. The first interview will take place following the completion of the first block of elective posting, and the second interview will take place following the conclusion of the second block of elective posting. Results: We observed a statistically significant difference when two blocks were compared on the domains of efforts. Students have reported higher efforts toward clinical and surgical postings. We also observed that students reported a statistically significant level of frustration in block two. In our study, we found that research work was taken more during the second block as students were sensitized in the first block regarding research. Conclusion: In our study, we have observed that research activities are taken up and processed for presentation using biostatistics in the second block of elective if resource material and support are provided in the block one. Students develop a better understanding of research with proper training. [ABSTRACT FROM AUTHOR]

Metabolomic analysis has been explored to search for disease biomarkers in humans for some time. The application to animal species, including fish, however, is still at the beginning. In the present study, we have used targeted and untargeted metabolomics to identify metabolites in the plasma of Atlantic salmon (Salmo salar) challenged with Piscine orthoreovirus (PRV-1), aiming to find metabolites associated with the progression of PRV-1 infection into heart and skeletal muscle inflammation (HSMI). The metabolomes of control and PRV-1-infected salmon were compared at three time points during disease development by employing different biostatistical approaches. Targeted metabolomics resulted in the determination of affected metabolites and metabolic pathways, revealing a substantial impact of PRV-1 infection on lipid homeostasis, especially on several (lyso)phosphatidylcholines, ceramides, and triglycerides. Untargeted metabolomics showed a clear separation of the treatment groups at later study time points, mainly due to effects on lipid metabolism pathways. In a subsequent multi-omics approach, we combined both metabolomics datasets with previously reported proteomics data generated from the same salmon plasma samples. Data processing with DIABLO software resulted in the identification of significant metabolites and proteins that were representative of the HSMI development in the salmon. [ABSTRACT FROM AUTHOR]

Metabolites are at the end of the gene–transcript–protein–metabolism cascade. As such, metabolomics is the omics approach that offers the most direct correlation with phenotype. This allows, where genomics, transcriptomics and proteomics fail to explain a trait, metabolomics to possibly provide an answer. Complex phenotypes, which are determined by the influence of multiple small-effect alleles, are an example of these situations. Consequently, the interest in metabolomics has increased exponentially in recent years. As a newer discipline, metabolomic bioinformatic analysis pipelines are not as standardized as in the other omics approaches. In this review, we synthesized the different steps that need to be carried out to obtain biological insight from annotated metabolite abundance raw data. These steps were grouped into three different modules: preprocessing, statistical analysis, and metabolic pathway enrichment. We included within each one of them the different state-of-the-art procedures and tools that can be used depending on the characteristics of the study, providing details about each method's characteristics and the issues the reader might encounter. Finally, we introduce genome-scale metabolic modeling as a tool for obtaining pseudo-metabolomic data in situations where their acquisition is difficult, enabling the analysis of the resulting data with the modules of the described workflow. [ABSTRACT FROM AUTHOR]

Graduates of Master of Public Health programs may lack appropriate skills in data analysis, and would benefit from practice with research data. Datasets that contain health information relevant to student interests, and that are appropriate sizes for class use can be difficult to locate. The National Health and Nutrition Examination Survey is a study that collects both survey and health examination information from a national sample every 2 years. I present a sample of this dataset, with examples of how to use it for human health related questions. Instructions for how to access and create additional, customized datasets are also provided. Instructors may consider investigating this rich data source and providing students with subsets of these data for class assignments, projects and master's theses. [ABSTRACT FROM AUTHOR]

One among several advantages of measure transport methods is that they allow or a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure. [ABSTRACT FROM AUTHOR]

Since the middle of the 20th century, oncology's dose-finding paradigm has been oriented toward identifying a drug's maximum tolerated dose, which is then carried forward into phase 2 and 3 trials and clinical practice. For most modern precision medicines, however, maximum tolerated dose is far greater than the minimum dose needed to achieve maximal benefit, leading to unnecessary side effects. Regulatory change may decrease maximum tolerated dose's predominance by enforcing dose optimization of new drugs. Dozens of already approved cancer drugs require re-evaluation, however, introducing a new methodologic and ethical challenge in cancer clinical trials. In this article, we assess the history and current landscape of cancer drug dose finding. We provide a set of strategic priorities for postapproval dose optimization trials of the future. We discuss ethical considerations for postapproval dose optimization trial design and review three major design strategies for these unique trials that would both adhere to ethical standards and benefit patients and funders. We close with a discussion of financial and reporting considerations in the realm of dose optimization. Taken together, we provide a comprehensive, bird's eye view of the postapproval dose optimization trial landscape and offer our thoughts on the next steps required of methodologies and regulatory and funding regimes. [ABSTRACT FROM AUTHOR]

Biostatistics has an important role in the education of medical and health professionals because it provides the necessary knowledge and skills to analyze biomedical data and critically read medical literature, which are key skills for practicing evidence-based medicine. Students encounter difficulties in learning this discipline related to complex calculations, formulas, limited time, lack of detailed explanations in online resources, weak conceptual foundations, understanding of rules and procedures. To address these difficulties, adapted learning and teaching strategies are recommended to improve students' skills in understanding and solving biostatistics problems. The aim of this paper is to present a design for teaching basic concepts of biostatistics that combines technologies and strategies in a manner tailored to student characteristics. As an example of basic concepts in the presented lesson unit, variables, data and scales for describing and measuring in statistics are selected. The educational goal was analyzed and lesson objectives were set. The didactic characteristics of the target group of students in master's programs in the fields of "Health Care" and "Public Health" were analyzed and conclusions were drawn about the design of the training. Some recommendations for developing interactive learning content are given. An example implementation of the lesson unit in the electronic learning content management system Moodle is presented. Careful design of course units, resulting from preliminary analyses, can make learning in traditionally difficult disciplines such as biostatistics for medical and health professionals not only enjoyable and easy, but also efficient and successful. [ABSTRACT FROM AUTHOR]

Introduction: Given the critical role of statistics in the analysis and interpretation of scientific data, majority of health care students are expected to acquire the statistics knowledge as a part of their curriculum. Despite the focus on the pertinence of statistics, students continue to struggle with understanding them. Hence, a qualitative study was performed to explore the undergraduate dental students' attitudes, perceptions and perceived barriers in learning and understanding statistics. Material and Methods Following purposive sampling technique, twenty-five undergraduate final year dental students were interviewed face to face or virtually using a standard general interview guide. The interviews were audio-taped and transcribed to verbatims. The data were subjected to descriptive and qualitative analysis to generate the themes by using the hybrid coding method. Results The mean age of the included participants was 23.12±1.013 years. The generated categories under the attitude component were interest, effort, difficulty, value and confidence. The themes generated under perception domain were content knowledge, importance, type of emotion and cognizance. The students opined that the barriers for learning and understanding statistics were related to time, curiosity, computation, teaching method and application. Conclusion The undergraduate dental students had an overall positive attitude towards statistics and their overall perception towards it was found to be good even though few of them had bad perception. The barriers reported by the students in learning and understanding statistics should be understood by the academic staff to make the student turn towards the desired direction by enhancing their skills. [ABSTRACT FROM AUTHOR]

The article discusses the importance of assessing heterogeneity of treatment effects (HTE) in clinical trials to understand variations in treatment responses across individuals. It highlights limitations of univariate subgroup analysis and introduces multivariable approaches to overcome these limitations. The article suggests grouping individuals based on phenotypes rather than individual characteristics and incorporating predictive HTE to improve interpretation. Practical considerations for conducting and interpreting HTE analyses are also provided, emphasizing the need for a patient-centered understanding of treatment benefits. [Extracted from the article]

This article reflects on the potential value and many pitfalls of underpowered studies to help authors and readers consider whether and how they contribute meaningfully to the published literature. A basic introduction to power and sample size calculations is provided. Several problems that can arise in analysis and publication of underpowered studies are described. In addition, features of underpowered studies that may provide value are proposed, including when the hypothesis test of interest is a limited part of the story, the data is rich enough to showcase interesting features of the population of interest, when the rarity or ubiquity of events is an important finding, and when the study is preregistered to reduce the impact of publication bias. Several reporting guidelines for underpowered studies are also suggested. [ABSTRACT FROM AUTHOR]

Metabolites are at the end of the gene–transcript–protein–metabolism cascade. As such, metabolomics is the omics approach that offers the most direct correlation with phenotype. This allows, where genomics, transcriptomics and proteomics fail to explain a trait, metabolomics to possibly provide an answer. Complex phenotypes, which are determined by the influence of multiple small-effect alleles, are an example of these situations. Consequently, the interest in metabolomics has increased exponentially in recent years. As a newer discipline, metabolomic bioinformatic analysis pipelines are not as standardized as in the other omics approaches. In this review, we synthesized the different steps that need to be carried out to obtain biological insight from annotated metabolite abundance raw data. These steps were grouped into three different modules: preprocessing, statistical analysis, and metabolic pathway enrichment. We included within each one of them the different state-of-the-art procedures and tools that can be used depending on the characteristics of the study, providing details about each method’s characteristics and the issues the reader might encounter. Finally, we introduce genome-scale metabolic modeling as a tool for obtaining pseudo-metabolomic data in situations where their acquisition is difficult, enabling the analysis of the resulting data with the modules of the described workflow.

Abstract Background A number of recommendations for the teaching of biostatistics have been published to date, however, student opinion on them has not yet been studied. For this reason, the aim of the manuscript was to find out the opinions of medical students at universities in Poland on two forms of teaching biostatistics, namely traditional and practical, as well as to indicate, on the basis of the results obtained, the related educational recommendations. Methods The study involved a group of 527 students studying at seven medical faculties in Poland, who were asked to imagine two different courses. The traditional form of teaching biostatistics was based on the standard teaching scheme of running a test from memory in a statistical package, while the practical one involved reading an article in which a particular test was applied and then applying it based on the instruction provided. Other aspects related to the teaching of the subject were assessed. Results According to the students of each course, the practical form of teaching biostatistics reduces the stress level associated with teaching and the student exam (p

AbstractMany types of health science learners, including clinical and translational scientists, students, researchers, and clinicians, seek to increase their knowledge of biostatistics. These learners are heterogeneous in their field, career stage and career focus. Based on the collective experience of an expert panel with over 115 years teaching statistics to health science learners, we propose a framework for considering the needs of health science learners motivated by their career goals. This framework defines four types of health science learners seeking statistical training: (a) consumers, (b) “milestone makers”, (c) biomedical researchers with statistical support, and (d) biomedical researchers without statistical support. Each type of learner has different levels at which they need to understand statistical topics for their careers, such as when to use a particular statistical method or why a given method works; these differing levels of understanding are detailed in our proposed framework. Further, this framework identifies the expectations that each of these types of learners should have for gaining statistical knowledge in a single seminar, multiple seminars, a seminar series, an accredited course, or a certificate/degree program. Advantages and disadvantages of widely used educational formats for these learners are also described. From this work, health science learners seeking biostatistical training or those who are planning a training program for others can gain insight into identifying appropriate statistical training goals for the type of learner with which they identify. Statistical educators may also use these guidelines to help health science learners align expectations for various types of training.

AbstractMost experimental medical research, if not all of it, heavily relies on statistical analysis. The difficulty of using statistical software is one of the key reasons why many researchers struggle with the data analysis stage. In this article, I discuss some of the user-friendly statistical programs that are accessible for free and ease data analysis for medical researchers and biostatisticians. Researchers can choose the best software by considering the descriptions of the programs, the statistical tests and features offered in each, the graphical features of each, and the strengths and weaknesses of each. Also, I will mention different references to help researchers use each statistical software adequately. Of note, this article reviews 7 statistical programs for use in medical research, namely JASP, Jamovi, BlueSky Statistics, Epi Info, Statistics Kingdom (web-based), SOFA Statistics, PSPP, and G*Power. For basic and common statistical tests, all the programs mentioned are useful and sufficient to complete the data analysis process. However, the best programs for advanced statistical methods are JASP, Jamovi, and BlueSky Statistics. G*Power is the program of choice for sample size and power analyses.