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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. [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]

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]

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]

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]

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.

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]

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.

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.

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]

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]

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]

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]

Purpose: As a part of STEPwise approach to risk factor Surveillance (STEPS) study, our aim was to evaluate the validity of the self-reported diagnosis of diabetes (DM), hypertension (HTN), and hypercholesterolemia (Hyper-Chol) in the Iranian population. Methods: Using systematic proportional to size cluster sampling, 27,232 participants were included in our study. We calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) to assess the validity of self-reported diagnoses. Furthermore, logistic regression was employed to examine the relationship between the validity of self-reported diagnoses and sociodemographic and lifestyle factors. All analyses were performed using STATA version 14. Results: The PPV for self-report of DM, HTN, and Hyper-Chol were estimated to be 69%, 74% and 80%, and NPV measured up to 95%, 84%, and 50%, respectively. Positive/negative self-reports were more accurate among older (younger) individuals. Age had a negative correlation with the validity of self-reported Hyper-Chol but a positive correlation with the validity of self-reported DM and hypertension HTN. Additionally, an increase in BMI was associated with an increase/decrease in PPV and a decrease/increase in NPV across all diseases. Conclusion: Self-report studies hold value in situations where direct in-person interaction is not feasible, either due to prohibitive costs or restrictions imposed by infectious diseases (COVID-19). Self-report surveys are valuable tools in studying the epidemiology of diseases; however, the type of the disease, the study purpose, either finding sick people or healthy people, the age subgroups, and socioeconomic status should be taken into consideration. [ABSTRACT FROM AUTHOR]

Early-onset breast cancer (EoBC), defined by a diagnosis <40 years of age, is associated with poor prognosis. This study investigated the mutational landscape of non-metastatic EoBC and the prognostic relevance of mutational signatures using 100 tumour samples from Alberta, Canada. The MutationalPatterns package in R/Bioconductor was used to extract de novo single-base substitution (SBS) and insertion–deletion (indel) mutational signatures and to fit COSMIC SBS and indel signatures. We assessed associations between these signatures and clinical characteristics of disease, in addition to recurrence-free (RFS) and overall survival (OS). Five SBS and two indel signatures were extracted. The SBS13-like signature had higher relative contributions in the HER2-enriched subtype. Patients with higher than median contribution tended to have better RFS after adjustment for other prognostic factors (HR = 0.29; 95% CI: 0.08–1.06). An unsupervised clustering algorithm based on absolute contribution revealed three clusters of fitted COSMIC SBS signatures, but cluster membership was not associated with clinical variables or survival outcomes. The results of this exploratory study reveal various SBS and indel signatures may be associated with clinical features of disease and prognosis. Future studies with larger samples are required to better understand the mechanistic underpinnings of disease progression and treatment response in EoBC. [ABSTRACT FROM AUTHOR]

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 < 0.001), as well as contributing to an increased level of elevated knowledge (p < 0.001), while the degree of satisfaction after passing the exam is higher (p < 0.001). A greater proportion of students (p < 0.001) believe that credit for the course could be given by doing a statistical review of an article or conducting a survey, followed by the tests learned in class. More than 95% also said that the delivery of the courses should be based on the field of study they were taking, during which time they would also like to have the opportunity to take part in optional activities and hear lectures from experts. Conclusion: It is recommended that more emphasis be placed on practical teaching the subject of biostatistics. [ABSTRACT FROM AUTHOR]

Publication bias describes a phenomenon in which significant positive results have a higher likelihood of being published compared to negative or nonsignificant results. Publication bias can confound the estimated therapeutic effect in meta-analyses and needs to be adequately assessed in the surgical literature. A review of meta-analyses published in five plastic surgery journals from 2002 to 2022 was conducted. The inclusion criteria for meta-analyses were factors that demonstrated an obligation to assess publication bias, such as interventions with comparable treatment groups and enough power for statistical analysis. Acknowledgment of publication bias risk, quality of bias assessment, methods used in assessment, and individual article factors were analyzed. 318 unique meta-analyses were identified in literature search, and after full-text reviews, 143 met the inclusion criteria for obligation to assess publication bias. 64% of eligible meta-analyses acknowledged the confounding potential of publication bias, and only 46% conducted a formal assessment. Of those who conducted an assessment, 49% used subjective inspection of funnel plots alone, while 47% used any statistical testing in analysis. Overall, only 9/143 (6.3%) assessed publication bias and attempted to correct for its effect. Journals with a higher average impact factor were associated with mention and assessment of publication bias, but more recent publication year and higher number of primary articles analyzed were not. This review identified low rates of proper publication bias assessment in meta-analyses published in five major plastic surgery journals. Assessment of publication bias using objective statistical testing is necessary to ensure quality literature within surgical disciplines. • Publication bias can compromise integrity of meta-analyses in surgical literature and needs to be adequately assessed using statistical methods. • The majority of meta-analysis articles published in plastic surgery literature used only unreliable subjective visual inspection methods to evaluate publication bias. • Fewer articles utilized objective statistical methods, such as the Egger's regression or Begg's rank-correlation test. • Higher impact journals in plastic surgery literature were more likely to mention and conduct a formal assessment of publication bias but were no more likely to use statistical methods in their assessment. • Meta-analyses and systematic reviews have dramatically increased in incidence, resulting in an imperative for investigators and readers to be able critically evaluate meta-analytic data. [ABSTRACT FROM AUTHOR]

There is a need for public health undergraduates to acquire skills in data collection, statistical programming, and infectious diseases modeling. Public health officials and accreditation bodies underline the importance of a cumulative, "real-world" experience as part of a student's education. The Watermelon Meow Meow (WMM) outbreak is a cumulative experience that teaches upper-level undergraduate/graduate students about infectious disease dynamics by asking students to: participate in a fictitious outbreak; collect and analyze outbreak data. Innovative to our approach is the use of DataCamp as a technology to support learning statistical programming and framing WMM under principles of Universal Design Learning (UDL). We evaluated 27/32 student responses using a mixed-methods approach. We found WMM: augmented traditional lecture-style instruction and increased student awareness of heterogeneous risks associated with infectious diseases. We identified three student typologies: students who learn best from (i) integrating traditional lecture plus WMM, (ii) participating in WMM data collection but not coding, and (iii) from lecture and classroom-based learning from peers. WMM is an example of a more general approach – which we call Slate, Operate, Translate – that instructors can follow to combine technology and a hands-on experiment to satisfy both UDL principles and increasing demands of public health education in a mathematics/statistics class. [ABSTRACT FROM AUTHOR]

IntroductionTreating 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 35×2 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.MethodsWe 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).ResultsModel 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.DiscussionThese findings are generally consistent with those of our previous mechanistic modeling, implying the potential benefits of altered radiotherapy fractionation schemes (e.g. 25×2.4 Gy, 20×2.75 Gy, 18×3.0 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.

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]

Ongoing professional development is important for collaborative biostatisticians, as it enables them to remain current with the latest advances in statistical methodology and software, refine their analytical skills, and expand their domain knowledge, thereby facilitating their ability to contribute effectively to biomedical research. Although external opportunities for professional development, such as attending conferences and workshops, are widely recognized and valued in the field of biostatistics, there has been comparatively little attention given to internal opportunities for enhancing the skills and knowledge of biostatisticians which can be implemented with lower financial and time investment than external offerings. The purpose of this paper is to offer guidance for ongoing internal professional development activities that can be employed by collaborative biostatistics units in universities and academic medical centers to complement structured curricula and initial training. Specific examples of activities are provided so that collaborative biostatisticians and/or managers of biostatistical units can flexibly combine components to create an appropriately scaled, customized program that meets the needs of themselves or of the unit.

BackgroundThe advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across studies, significantly limiting the number of questions that can be confidently addressed. We sought to develop a method for meta-analyzing potentially heterogeneous clinical trials even in the absence of a common control group.MethodsThis work was conducted within the context of a broader effort to study comparative efficacy in Crohn's disease. Following a search of clnicaltrials.gov we obtained access to the individual participant data from nine trials of FDA-approved treatments in Crohn's Disease (N = 3392). We developed a method involving sequences of regression and simulation to separately model the placebo- and drug-attributable effects, and to simulate head-to-head trials against an appropriately normalized background. We validated this method by comparing the outcome of a simulated trial comparing the efficacies of adalimumab and ustekinumab against the recently published results of SEAVUE, an actual head-to-head trial of these drugs. This study was pre-registered on PROSPERO (#157,827) prior to the completion of SEAVUE.ResultsUsing our method of sequential regression and simulation, we compared the week eight outcomes of two virtual cohorts subject to the same patient selection criteria as SEAVUE and treated with adalimumab or ustekinumab. Our primary analysis replicated the corresponding published results from SEAVUE (p = 0.9). This finding proved stable under multiple sensitivity analyses.ConclusionsThis new method may help reduce the bias of individual participant data meta-analyses, expand the scope of what can be learned from these already-collected data, and reduce the costs of obtaining high-quality evidence to guide patient care.

Cancer causes immense suffering globally, and data constitute the cornerstone of cancer research. Analyzing data is pivotal, but manual analysis of vast datasets within constrained time frames is challenging and error-prone. Even minor inaccuracies can lead to false interpretations, affecting lives. This review explores the free, open-source, and widely acclaimed R software. Our goal was to facilitate data analysis and visualization in the scientific writing of clinical projects. R offers a wide range of features and packages for tasks like data manipulation, cleaning, analysis, and creating informative graphs, including traditional statistics, hypothesis testing, regression, time series, survival analysis, machine learning, and medical image analysis. These capabilities aid in accurate data analysis, facilitating a deeper understanding of cancer mechanisms and predicting outcomes. To prepare this review, we performed an online literature search in Scopus, PubMed, and Google for articles and books related to R software published between March 2012 and January 2024, using specific keywords such as “medical data analysis,” “RStudio,” “statistical software,” “clinical data management,” “R programming,” and “research tools.” Articles, books, and online sources lacking full-text options in English or complete information were excluded. A total of 66 articles and book chapters were retrieved, 22 were excluded, and 44 were included in this review. Through this article, our goal was to provide a user-friendly guide to employing R software for fundamental analysis with dummy data, making it accessible even to non-programmers. This will empower individuals to perform statistical analyses independently, contributing to cancer research with flexibility and accuracy.

Abstract Background Calls for improved statistical literacy and transparency in population health research are widespread, but empirical accounts describing how researchers understand statistical methods are lacking. To address this gap, this study aimed to explore variation in researchers’ interpretations and understanding of regression coefficients, and the extent to which these statistics are viewed as straightforward statements about health. Methods Thematic analysis of qualitative data from 45 one-to-one interviews with academics from eight countries, representing 12 disciplines. Three concepts from the sociology of scientific knowledge and science studies aided analysis: Duhem’s Paradox, the Agonistic Field, and Mechanical Objectivity. Results Some interviewees viewed regression as a process of discovering ‘real’ relationships, while others indicated that regression models are not direct representations, and others blended these perspectives. Regression coefficients were generally not viewed as being mechanically objective, instead interpretation was described as iterative, nuanced, and sometimes depending on prior understandings. Researchers reported considering numerous factors when interpreting and evaluating regression results, including: knowledge from outside the model, whether results are expected or unexpected, ‘common-sense’, technical limitations, study design, the influence of the researcher, the research question, data quality and data availability. Interviewees repeatedly highlighted the role of the analyst, reinforcing that it is researchers who answer questions and assign meaning, not models. Conclusions Regression coefficients were generally not viewed as complete or authoritative statements about health. This contrasts with teaching materials wherein statistical results are presented as straightforward representations, subject to rule-based interpretations. In practice, it appears that regression coefficients are not understood as mechanically objective. Attempts to influence conduct and presentation of regression models in the population health sciences should be attuned to the myriad factors which inform their interpretation.

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.

Background: This research evaluates the results of a distance biostatistics training for physicians during the COVID-19 pandemic. Materials and Method: Online basic-level biostatistics training consisting of six courses was conducted between 21 October-30 December 2020. At the beginning and after the training, the structured Biostatistics Basic Knowledge Questionnaire consisting of 20 questions developed by the researchers was applied to the physicians who took the training. Results: A total of 198 medical doctors participated. At the end of the training, 21 of those who participated in the research at the beginning attended more than half of the courses, 45 of them attended less than half of the courses, and 132 physicians did not attend any of the courses. 80.8% of the participants stated that they took the training to make their statistics in their scientific research. After the training, regardless of their professional title and department, the score change was not significant for those who did not attend the classes at all, (p=0.306), while it was found that the score increased significantly for those who attended less than half and those who attended more than half of them (p<0.001 for both). Conclusion: The high achievement of even those who attended only some of the courses after the training shows that such distance education models should be developed and widely extended. [ABSTRACT FROM AUTHOR]

Aim: To illustrate the use of joint models (JMs) for longitudinal and survival data in estimating risk factors of tooth loss as a function of time‐varying endogenous periodontal biomarkers (probing pocket depth [PPD], alveolar bone loss [ABL] and mobility [MOB]). Materials and Methods: We used data from the Veterans Affairs Dental Longitudinal Study, a longitudinal cohort study of over 30 years of follow‐up. We compared the results from the JM with those from the extended Cox regression model which assumes that the time‐varying covariates are exogenous. Results: Our results showed that PPD is an important risk factor of tooth loss, but each model produced different estimates of the hazard. In the tooth‐level analysis, based on the JM, the hazard of tooth loss increased by 4.57 (95% confidence interval [CI]: 2.13–8.50) times for a 1‐mm increase in maximum PPD, whereas based on the extended Cox model, the hazard of tooth loss increased by 1.60 (95% CI: 1.37–1.87) times. Conclusions: JMs can incorporate time‐varying periodontal biomarkers to estimate the hazard of tooth loss. As JMs are not commonly used in oral health research, we provide a comprehensive set of R codes and an example dataset to implement the method. [ABSTRACT FROM AUTHOR]

Conventional agriculture is reliant on the application of unsustainably high rates of inorganic fertilizers with detrimental consequences on the environment. The use of invasive aquatic macrophytes as organic fertilizer for crop production represents a unique opportunity for weed control as well as to use this plant material that would typically be disposed of, as a resource instead. Therefore, the goal of the present study was to use Pistia stratiotes and Eichhornia crassipes as organic fertilizer for the growth of a high value vegetable, Capsicum annuum. Furthermore, to evaluate if Euclidean distance can be used as a tool to integrate the data collected into a single meaningful outcome. The results showed that growth on soil amended with plant material from the aquatic macrophytes resulted in improved plant height, number of leaves per plant, leaf length, stalk diameter and the number of fruits produced per plant (characterized by Medium and High OCVs). The outcome from calculation of the Euclidean distance revealed that the best treatments to achieve the desired phenotype were amendments with 100 g P. stratiotes or 400 g E. crassipes. Therefore, it was evident that the tested macrophyte weeds can be used as organic fertilizers to promote plant growth and yield in C. annuum and that assessment of the Euclidean distance is a useful tool to incorporate the findings from a range of parameters into a single summarized outcome. [ABSTRACT FROM AUTHOR]

It is important for young people to be aware of job profiles and activities in the professional world. Bringing the education system closer to the professional world is vital for them to make decisions about their academic and professional futures. Programs developed to connect 15‐year‐old students who in Spain are in year 4 of their Compulsory Secondary Education, and Research Support Units within the Health Research Institutes of the Hospitals and the Clinical Research Support Platforms of the Carlos III Institute of Health are a good opportunity to highlight the role of biostatistics in clinical trials. The aim of this article is to share the outcomes of and learnings from an interactive workshop for 15‐year‐old students on biostatistics and clinical trials conducted within the 4°ESO + Empresa program and directed by the Scientific Support Unit of the Health Research Institute of Hospital 12 de Octubre in Madrid, Spain. [ABSTRACT FROM AUTHOR]

This paper examines the application of super-superiority margins in study power calculations. Unlike traditional power calculations, which primarily aim to reject the null hypothesis by any margin, a super-superiority margin establishes a clinically significant threshold. Despite potential benefits, this approach, akin to a non-inferiority calculation but in an opposing direction, is rarely used. Implementing a super-superiority margin separates the notion of the likely difference between two groups (the effect size) from the minimum clinically significant difference, without which inconsistent positions could be held. However, these are often used interchangeably. In an audit of 30 recent randomized controlled trial power calculations, four studies utilized the minimal acceptable difference, and nine utilized the expected difference. In the other studies, this was unclarified. In the post hoc scenario, this approach can shed light on the value of undertaking further studies, which is not apparent from the standard power calculation. The acceptance and rejection of the alternate hypothesis for super-superiority, non-inferiority, equivalence, and standard superiority studies have been compared. When a fixed minimal acceptable difference is applied, a study result will be in one of seven logical positions with regards to the simultaneous application of these hypotheses. The trend for increased trial size and the mirror approach of non-inferiority studies implies that newer interventions may be becoming less effective. Powering for superiority could counter this and ensure that a pre-trial evaluation of clinical significance has taken place, which is necessary to con- firm that interventions are beneficial. [ABSTRACT FROM AUTHOR]

The flipped classroom (FC) is a pedagogical model with an active learning concept and a hybrid course design that reverses the typical lecture process. Given the widespread use of online delivery methods, there is a need to explore the FC in delivering statistics courses. This study employs a prospective quasi-experimental study design to evaluate the effectiveness of the flipped method and to compare it with the traditional method of teaching statistical data analysis software. Each of the 71 students enrolled in a medicine program was allocated to either a traditional classroom or a FC. The difference between the two teaching methods is evaluated using overall assessment performance as the main outcome measure. Our results show that the teaching method had a large and significant effect on assessment performance, with the FC method exhibiting a higher student overall performance than the traditional classroom. The data suggest integrating the FC in teaching statistical data analysis software is a helpful alternative to the traditional classroom. This study may serve as a guide and inspiration for educators in statistics courses to incorporate the concept of flipped learning, so as to engage students in a more active learning experience. [ABSTRACT FROM AUTHOR]

Background: Calls for improved statistical literacy and transparency in population health research are widespread, but empirical accounts describing how researchers understand statistical methods are lacking. To address this gap, this study aimed to explore variation in researchers' interpretations and understanding of regression coefficients, and the extent to which these statistics are viewed as straightforward statements about health. Methods: Thematic analysis of qualitative data from 45 one-to-one interviews with academics from eight countries, representing 12 disciplines. Three concepts from the sociology of scientific knowledge and science studies aided analysis: Duhem's Paradox, the Agonistic Field, and Mechanical Objectivity. Results: Some interviewees viewed regression as a process of discovering 'real' relationships, while others indicated that regression models are not direct representations, and others blended these perspectives. Regression coefficients were generally not viewed as being mechanically objective, instead interpretation was described as iterative, nuanced, and sometimes depending on prior understandings. Researchers reported considering numerous factors when interpreting and evaluating regression results, including: knowledge from outside the model, whether results are expected or unexpected, 'common-sense', technical limitations, study design, the influence of the researcher, the research question, data quality and data availability. Interviewees repeatedly highlighted the role of the analyst, reinforcing that it is researchers who answer questions and assign meaning, not models. Conclusions: Regression coefficients were generally not viewed as complete or authoritative statements about health. This contrasts with teaching materials wherein statistical results are presented as straightforward representations, subject to rule-based interpretations. In practice, it appears that regression coefficients are not understood as mechanically objective. Attempts to influence conduct and presentation of regression models in the population health sciences should be attuned to the myriad factors which inform their interpretation. [ABSTRACT FROM AUTHOR]