Incorporating equity, diversity, and inclusion into the epidemiology and biostatistics curriculum: A workshop report and implementation strategies recommendations.

There is an obligation among those teaching epidemiology to incorporate principles of equity, diversity, and inclusion (EDI) into the curriculum. While there is a well-established literature related to teaching epidemiology, this literature rarely addresses critical aspects of EDI. To our knowledge, there is no working group or central point of discussion and learning for incorporating EDI into epidemiology teaching in Canada. To address this gap, we convened a workshop entitled "Incorporating EDI into the epidemiology and biostatistics curriculum and classroom." The workshop discussed nine strategies to incorporate EDI in the epidemiology curriculum: positionality (or reflexivity) statements; opportunities for feedback; land acknowledgements; clarifying the purpose of collecting data on race and ethnicity, sex and gender, Indigeneity; acknowledging that race/ethnicity is a social construct, not a biological variable; describing incidence and prevalence of disease; demonstrating explicit bias using directed acyclic graphs (DAGs); critical appraisal of study population diversity; and admission criteria and considerations. Key take-aways from the workshop were the need to be more intentional when determining the validity of evidence, particularly with respect to historical context and the need to recognize that there is no single solution that will address EDI.

RéSUMé: Les personnes qui enseignent l'épidémiologie ont l'obligation d'intégrer les principes d'équité, de diversité et d'inclusion (EDI) dans le programme d'études. Bien qu'il existe une littérature bien établie sur l'enseignement de l'épidémiologie, cette littérature aborde rarement les aspects critiques de l'EDI. À notre connaissance, il n'existe pas de groupe de travail ou de point central de discussion et d'apprentissage pour l'intégration de l'EDI dans l'enseignement de l'épidémiologie au Canada. Pour combler cette lacune, nous avons organisé un atelier intitulé « Incorporer l'EDI dans le programme d'enseignement de l'épidémiologie et de la biostatistique et dans la salle de classe ¼. L'atelier a examiné neuf stratégies visant à intégrer l'EDI dans le programme d'enseignement de l'épidémiologie : déclarations de positionnement (ou de réflexivité); occasions pour partager de la rétroaction; reconnaissances territoriales; clarification de l'objectif derrière la collecte de données sur la race et l'ethnicité, le sexe et le genre et l'indigénéité; reconnaissance du fait que la race/l'ethnicité est une construction sociale et non une variable biologique; description de l'incidence et de la prévalence des maladies; démonstration de parti pris explicites à l'aide de graphe orienté acyclique (DAG); évaluation critique de la diversité de l'échantillon étudié; et critères et considérations d'admission. Les principaux enseignements tirés de l'atelier sont la nécessité d'être plus intentionnel dans la détermination de la validité des données probantes, en particulier en ce qui concerne le contexte historique, et la nécessité de reconnaître qu'il n'existe pas de solution unique pour prendre en compte les principes de l'EDI.

Just how transformative will AI/ML be for immuno-oncology?

Immuno-oncology involves the study of approaches which harness the patient's immune system to fight malignancies. Immuno-oncology, as with every other biomedical and clinical research field as well as clinical operations, is in the midst of technological revolutions, which vastly increase the amount of available data. Recent advances in artificial intelligence and machine learning (AI/ML) have received much attention in terms of their potential to harness available data to improve insights and outcomes in many areas including immuno-oncology. In this review, we discuss important aspects to consider when evaluating the potential impact of AI/ML applications in the clinic. We highlight four clinical/biomedical challenges relevant to immuno-oncology and how they may be able to be addressed by the latest advancements in AI/ML. These challenges include (1) efficiency in clinical workflows, (2) curation of high-quality image data, (3) finding, extracting and synthesizing text knowledge as well as addressing, and (4) small cohort size in immunotherapeutic evaluation cohorts. Finally, we outline how advancements in reinforcement and federated learning, as well as the development of best practices for ethical and unbiased data generation, are likely to drive future innovations.

A retrospective and prospective study of biostatistics in Canada.

Biostatistics is foundational to public health research and Canada has a history of high impact contributions both in seminal methodological advances and in the rigorous application of methods for the design or analysis of public health studies. In this article, we provide a brief and personal review of selected contributions from Canadian biostatisticians to fields such as survival and life history analysis, sampling, clinical trial methodology, environmental risk assessment, infectious disease epidemiology, and early work on prediction. We also provide a brief look forward at the upcoming needs and future directions of biostatistical research.

RéSUMé: La biostatistique est fondamentale pour la recherche en santé publique et le Canada a un historique de contributions à fort impact, tant dans les avancées méthodologiques majeures que dans l'application rigoureuse de méthodes pour la conception ou l'analyse d'études de santé publique. Dans cet article, nous présentons un examen bref et personnel des contributions des biostatisticiens canadiens dans des domaines tels que l'analyse de la survie et de l'histoire de vie, l'échantillonnage, la méthodologie des essais cliniques, le risque environnemental, l'épidémiologie des maladies infectieuses et les premiers travaux sur la prédiction et la classification. Nous fournissons également un bref aperçu des besoins à venir et des orientations futures de la recherche biostatistique.

Developing and Validating Clinical Prediction Models in Hepatology-an Overview for Clinicians.

Prediction models are everywhere in clinical medicine. We use them to assign a diagnosis or a prognosis, and there is a continuous effort to develop better prediction models. It is important to understand the fundamentals of prediction modeling, and here we describe nine steps to develop and validate a clinical prediction model with the intention of implementing it in clinical practice: Determine if there is a need for a new prediction model; define the purpose and intended use for the model; assess the quality and quantity of the data you wish to develop the model on; develop the model using sound statistical methods; generate risk predictions on the probability scale (0-100%); evaluate the performance of the model in terms of discrimination, calibration, and clinical utility; validate the model using bootstrapping to correct for the apparent optimism in performance; validate the model on external datasets to assess the generalizability and transportability of the model; and finally publish the model so that it can be implemented or validated by others.

Income disparities in loss in life expectancy after colon and rectal cancers: a Swedish register-based study.

BACKGROUND: Differences in the prognosis after colorectal cancer (CRC) by socioeconomic position (SEP) have been reported previously; however, most studies focused on survival differences at a particular time since diagnosis. We quantified the lifetime impact of CRC and its variation by SEP, using individualised income to conceptualise SEP. METHODS: Data included all adults with a first-time diagnosis of colon or rectal cancers in Sweden between 2008 and 2021. The analysis was done separately for colon and rectal cancers using flexible parametric models. For each cancer and income group, we estimated the life expectancy in the absence of cancer, the life expectancy in the presence of cancer and the loss in life expectancy (LLE). RESULTS: We found large income disparities in life expectancy after a cancer diagnosis, with larger differences among the youngest patients. Higher income resulted in more years lost following a cancer diagnosis. For example, 40-year-old females with colon cancer lost 17.64 years if in the highest-income group and 13.68 years if in the lowest-income group. Rectal cancer resulted in higher LLE compared with colon cancer. Males lost a larger proportion of their lives. All patients, including the oldest, lost more than 30% of their remaining life expectancy. Based on the number of colon and rectal cancer diagnoses in 2021, colon cancer results in almost double the number of years lost compared with rectal cancer (24 669 and 12 105 years, respectively). CONCLUSION: While our results should be interpreted in line with what individualised income represents, they highlight the need to address inequalities.

Integrating Fréchet distance and AI reveals the evolutionary trajectory and origin of SARS-CoV-2.

A genome, composed of a precisely ordered sequence of four nucleotides (ATCG), encompasses a multitude of specific genome features like AAA motif. Mutations occurring within a genome disrupt the sequential order and composition of these features, thereby influencing the evolutionary trajectories and yielding variants. The evolutionary relatedness between a variant and its ancestor can be estimated by assessing evolutionary distances across a spectrum of genome features. This study develops a novel, alignment-free algorithm that considers both the sequential order and composition of genome features, enabling computation of the Fréchet distance (Fr) across multiple genome features to quantify the evolutionary status of a variant. Integrating this algorithm with an artificial recurrent neural network (RNN) reveals the quantitative evolutionary trajectory and origin of SARS-CoV-2, a puzzle unsolved by alignment-based phylogenetics. The RNN generates the evolutionary trajectory from Fr data at two levels: genome sequence mutations and organism variants. At the genome sequence level, SARS-CoV-2 evolutionarily shortens its genome to enhance its infectious capacity. Mutating signature features, such as TTA and GCT, increases its infectious potential and drives its evolution. At the organism level, variants mutating a single biomarker possess low infectious potential. However, mutating multiple markers dramatically increases their infectious capacity, propelling the COVID-19 pandemic. SARS-CoV-2 likely originates from mink coronavirus variants, with its origin trajectory traced as follows: mink, cat, tiger, mouse, hamster, dog, lion, gorilla, leopard, bat, and pangolin. Together, mutating multiple signature features and biomarkers delineates the evolutionary trajectory of mink-origin SARS-CoV-2, leading to the COVID-19 pandemic.

Detection of SARS-CoV-2 virus in middle ear effusions and its association with otitis media with effusion.

A large-scale outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) occurred in Shanghai, China, in early December 2022. To study the incidence and characteristics of otitis media with effusion (OME) complicating SARS-CoV-2, we collected 267 middle ear effusion (MEE) samples and 172 nasopharyngeal (NP) swabs from patients. The SARS-CoV-2 virus was detected by RT-PCR targeting. The SARS-CoV-2 virus, angiotensin-converting enzyme 2 (ACE2), and transmembrane serine protease 2 (TMPRSS2) expression in human samples was examined via immunofluorescence. During the COVID-19 epidemic in 2022, the incidence of OME (3%) significantly increased compared to the same period from 2020 to 2022. Ear symptoms in patients with SARS-CoV-2 complicated by OME generally appeared late, even after a negative NP swab, an average of 9.33 ± 6.272 days after COVID-19 infection. The SARS-CoV-2 virus was detected in MEE, which had a higher viral load than NP swabs. The insertion rate of tympanostomy tubes was not significantly higher than in OME patients in 2019-2022. Virus migration led to high viral loads in MEE despite negative NP swabs, indicating that OME lagged behind respiratory infections but had a favorable prognosis. Furthermore, middle ear tissue from adult humans coexpressed the ACE2 receptor for the SARS-CoV-2 virus and the TMPRSS2 cofactors required for virus entry.

Bayesian estimation in veterinary pharmacology: A conceptual and practical introduction.

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.

Designing AI for mental health diagnosis: challenges from sub-Saharan African value-laden judgements on mental health disorders.

Recently clinicians have become more reliant on technologies such as artificial intelligence (AI) and machine learning (ML) for effective and accurate diagnosis and prognosis of diseases, especially mental health disorders. These remarks, however, apply primarily to Europe, the USA, China and other technologically developed nations. Africa is yet to leverage the potential applications of AI and ML within the medical space. Sub-Saharan African countries are currently disadvantaged economically and infrastructure-wise. Yet precisely, these circumstances create significant opportunities for the deployment of medical AI, which has already been deployed in some places in the continent. However, while AI and ML have come with enormous promises in Africa, there are still challenges when it comes to successfully applying AI and ML designed elsewhere within the African context, especially in diagnosing mental health disorders. We argue, in this paper, that there ought not to be a homogeneous/generic design of AI and ML used in diagnosing mental health disorders. Our claim is grounded on the premise that mental health disorders cannot be diagnosed solely on 'factual evidence' but on both factual evidence and value-laden judgements of what constitutes mental health disorders in sub-Saharan Africa. For ML to play a successful role in diagnosing mental health disorders in sub-Saharan African medical spaces, with a precise focus on South Africa, we allude that it ought to understand what sub-Saharan Africans consider as mental health disorders, that is, the value-laden judgements of some conditions.

Looking beyond the mean: quantile regression for comparative physiologists.

Statistical analyses that physiologists use to test hypotheses predominantly centre on means, but the tail ends of the response distribution can behave quite differently and underpin important scientific phenomena. We demonstrate that quantile regression (QR) offers a way to bypass some limitations of least squares regression (LSR) by building a picture of independent variable effects across the whole distribution of a dependent variable. We used LSR and QR with simulated and real datasets. With simulated data, LSR showed no change in the mean response but missed significant effects in the tails of the distribution found using QR. With real data, LSR showed a significant change in the mean response but missed a lack of response in the upper quantiles which was biologically revealing. Together, this highlights that QR can help to ask and answer more questions about variation in nature.

Peer review of clinical and translational research manuscripts: Perspectives from statistical collaborators.

Research articles in the clinical and translational science literature commonly use quantitative data to inform evaluation of interventions, learn about the etiology of disease, or develop methods for diagnostic testing or risk prediction of future events. The peer review process must evaluate the methodology used therein, including use of quantitative statistical methods. In this manuscript, we provide guidance for peer reviewers tasked with assessing quantitative methodology, intended to complement guidelines and recommendations that exist for manuscript authors. We describe components of clinical and translational science research manuscripts that require assessment including study design and hypothesis evaluation, sampling and data acquisition, interventions (for studies that include an intervention), measurement of data, statistical analysis methods, presentation of the study results, and interpretation of the study results. For each component, we describe what reviewers should look for and assess; how reviewers should provide helpful comments for fixable errors or omissions; and how reviewers should communicate uncorrectable and irreparable errors. We then discuss the critical concepts of transparency and acceptance/revision guidelines when communicating with responsible journal editors.

Adapting power calculations to include a superiority margin: what are the implications?

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 confirm that interventions are beneficial.

Social Media Posts About Carpal Tunnel Release: A Cross-Sectional Analysis of Patient and Surgeon Perspectives.

BACKGROUND: Social media provides an increasingly popular, unfiltered source of perspectives on healthcare. The objective of this study is to characterize the landscape of social media posts regarding carpal tunnel release (CTR). METHODS: Content was queried from Instagram between February 2, 2019 to August 12, 2021 using the hashtags #carpaltunnelrelease and #carpaltunnelsurgery. The 1500 most-liked posts were analyzed. Poster demographics including age, gender, region, and symptom qualities and post characteristics including type, number, timing relative to surgery, tone, and satisfaction were collected. Categorical variables were compared utilizing chi-squared test. Univariate and multivariate regression were performed. RESULTS: The most popular post types included single photo (55.2%), multiple photos (18.8%), or single video (18.2%). Of all, 70.6% posts had fewer than 50 "likes." Patients accounted for 51.8% of posts, followed by surgeons (13.3%), other health care providers (11.7%), and physical therapists (8.8%). Women (66.7%) outnumbered men (33.3%). Fifty-five percent of posts were domestic. Posts mostly depicted postoperative care (85.6%). The most frequently mentioned symptoms were pain, burning, numbness, and tingling. Of all posts, 45.1% had a positive tone, 49.1% neutral, and 5.7% negative. Univariate analysis revealed that posters who were patients, underwent open CTR, and were female were more likely to post negative sentiments. CONCLUSIONS: Most posts regarding CTR are from patients, are postoperative, and are positive or neutral. Although rare, negative posts were more likely to originate from posters who are patients, female, or underwent open CTR. With this information, surgeons will be better prepared to address patient concerns, set patient expectations, and enter the social media themselves.

Basic Statistics, Statistical Design, and Critical Analysis of Statistics for Surgeons.

Statistics is a set of tools used in medical decision-making no different than how a scalpel or a sagittal saw is used in the operating room. No foot and ankle surgeon is born with the inherent ability to perform, understand, and critically interpret them. Instead, it requires training and practice throughout the course of a career in medicine to develop a working proficiency. This article reviews the basic indications and interpretation of common descriptive and comparative statistical tests in the podiatric literature. Additionally, the concept of which tests are most appropriate for which investigational methodologies is introduced.

Data Distribution: Normal or Abnormal?

Determining if the frequency distribution of a given data set follows a normal distribution or not is among the first steps of data analysis. Visual examination of the data, commonly by Q-Q plot, although is acceptable by many scientists, is considered subjective and not acceptable by other researchers. One-sample Kolmogorov-Smirnov test with Lilliefors correction (for a sample size ≥ 50) and Shapiro-Wilk test (for a sample size < 50) are common statistical tests for checking the normality of a data set quantitatively. As parametric tests, which assume that the data distribution is normal (Gaussian, bell-shaped), are more robust compared to their non-parametric counterparts, we commonly use transformations (e.g., log-transformation, Box-Cox transformation, etc.) to make the frequency distribution of non-normally distributed data close to a normal distribution. Herein, I wish to reflect on presenting how to practically work with these statistical methods through examining of real data sets.

Subarachnoid Hemorrhage Trials: Cutting, Sliding, or Keeping mRS Scores and WFNS Grades.

Rigorous evidence generation with randomized controlled trials has lagged for aneurysmal subarachnoid hemorrhage (SAH) compared with other forms of acute stroke. Besides its lower incidence compared with other stroke subtypes, the presentation and outcome of patients with SAH also differ. This must be considered and adjusted for in designing pivotal randomized controlled trials of patients with SAH. Here, we show the effect of the unique expected distribution of the SAH severity at presentation (World Federation of Neurological Surgeons grade) on the outcome most used in pivotal stroke randomized controlled trials (modified Rankin Scale) and, consequently, on the sample size. Furthermore, we discuss the advantages and disadvantages of different options to analyze the outcome and control the expected distribution of the World Federation of Neurological Surgeons grades in addition to showing their effects on the sample size. Finally, we offer methods that investigators can adapt to more precisely understand the effect of common modified Rankin Scale analysis methods and trial eligibility pertaining to the World Federation of Neurological Surgeons grade in designing their large-scale SAH randomized controlled trials.