Impact of patient selection in clinical trials: application of ROCKET AF and ARISTOTLE criteria in GARFIELD-AF.

BACKGROUND: The extent to which differences in results from Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE) and Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial (ROCKET) atrial fibrillation (AF)-the landmark trials for the approval of apixaban and rivaroxaban, respectively, for non-valvular AF-were influenced by differences in their protocols is debated. The potential influence of selection criteria on trial results was assessed by emulating these trials in data from the Global Anticoagulant Registry in the Field (GARFIELD)-AF registry. METHODS: Vitamin K antagonist (VKA) and non-vitamin K oral antagonist (NOAC) users from GARFIELD-AF were selected according to eligibility for the original ARISTOTLE or ROCKET AF trials. A propensity score overlap weighted Cox model was used to emulate trial randomisation between treatment groups. Adjusted HRs for stroke or systemic embolism (SE) within 2 years of enrolment were calculated for each NOAC versus VKA. RESULTS: Among patients on apixaban, rivaroxaban and VKA, 2570, 3560 and 8005 were eligible for ARISTOTLE, respectively, and 1612, 2005 and 4368, respectively, for ROCKET AF. When selecting for ARISTOTLE criteria, apixaban users had significantly lower stroke/SE risk versus VKA (HR 0.57; 95% CI 0.34 to 0.94) while no reduction was observed with rivaroxaban (HR 0.98; 95% CI 0.68 to 1.40). When selecting for ROCKET AF criteria, safety and efficacy versus VKA were similar across the NOACs. CONCLUSION: Apixaban and rivaroxaban showed similar results versus VKA in high-risk patients selected according to ROCKET AF criteria, whereas differences emerged when selecting for the more inclusive ARISTOTLE criteria. Our results highlight the importance of trial selection criteria in interpreting trial results and underline the problems faced in comparing treatments across rather than within clinical trials.

The Conclusion Generator.

PURPOSE: 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. METHODS: We reviewed the content of the Conclusion Generator and validated its output using published and simulated data. RESULTS: 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. CONCLUSIONS: The Conclusion Generator facilitates an appropriate interpretation of research results by emphasizing estimation and clinical relevance over hypothesis testing.

Metaproteomic Analysis of Biogas Plants: A Complete Workflow from Lab to Bioinformatics.

Metaproteomics represents a promising and fast method to analyze the taxonomic and functional composition of biogas plant microbiomes. However, metaproteomics sample preparation and bioinformatics analysis is still challenging due to the sample complexity and contaminants. In this chapter, a tailored workflow including sampling, phenol extraction in a ball mill, amido black protein quantification, FASP digestion, LC-MS/MS measurement as well as bioinformatics and biostatistical data evaluation are here described for the metaproteomics advancements applied to biogas plant samples.

Ten things to remember about propensity scores.

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.

Assessing attitudes towards biostatistics education among medical students: adaptation and preliminary evaluation of the Chinese version survey of attitudes towards statistics (SATS-36).

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.

Validation of the self-reported diagnosis of diabetes mellitus, hypercholesterolemia, and hypertension in Iran; STEPS 2016.

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.

Species-level characterization of the cervicovaginal microbiota and its role in human papillomavirus-associated cervical carcinogenesis.

The cervicovaginal microbiome may contribute to human papillomavirus (HPV)-associated cervical carcinogenesis, but studies have been limited by low-resolution analysis methods. Using a high-resolution bioinformatics pipeline, we evaluated the relationship of the cervicovaginal microbiome with HPV and cervical intraepithelial neoplasia (CIN). The cervicovaginal microbiome of 186 women was characterized by sequencing 16S rRNA regions (V3-V4 and V5-V6) and annotated with the high-resolution ANCHOR pipeline. Samples were genotyped for HPV using the Roche-Cobas 4800 assay. We fitted logistic regression models using stepwise forward selection to select species (presence/absence) as correlates of CIN1+ and constructed a linear microbiome-based score using the regression coefficients. An HPV-based score was calculated from a separate logistic regression model to detect CIN1+ . Receiver operating characteristic curve analyses were performed; the area under the curve (AUC) and 95% confidence intervals (CI) were compared between scores. Overall, 66.7% of participants were HPV-positive. 77 unique species were identified: 8 using V3-V4, 48 using V5-V6, and 21 shared. Twelve species were retained via stepwise selection. The AUCs for the microbiome-, and HPV-based scores were 0.7656 (95% CI 0.6885-0.8426), and 0.7529 (95% CI 0.6855-0.8204), respectively. Bacterial species may be involved in cervical carcinogenesis as the microbiome- and HPV-based scores performed similarly for CIN1+ detection.

The Association between Mutational Signatures and Clinical Outcomes among Patients with Early-Onset Breast Cancer.

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.

Analysis of core outcome set reporting in coronary intervention trials.

BACKGROUND: This paper will focus on outcome reporting within percutaneous coronary intervention (PCI) trials. A core outcome set (COS) is a standardised set of outcomes that are recommended to be reported in every clinical trial. Using a COS can help to ensure that all relevant outcomes are consistently reported across clinical trials. In 2018, the European Society of Cardiology outlined the only COS published for PCI trials. METHODS: We searched the literature for all randomised controlled trials published between 2014 and 2022. PCI trials included were late-phase trials and must investigate coronary intervention. The primary outcome was the proportion of trials that reported all of the COS-defined outcomes within their publication as either a primary, secondary or safety endpoint. The secondary outcomes included; the number of primary outcomes reported per study, the proportion of studies which use patient and public involvement (PPI) during trial design, outcome variability and outcome consistency. RESULTS: 9580 trials were screened and 115 studies met inclusion/exclusion criteria. Our study demonstrated that 55% (34/62) of PCI trials used a COS when it was available, compared with 40% (21/53) before the availability of a PCI COS set, p=0.121. Fewer primary outcomes were reported after the implementation of the COS, 2 compared with 2.3, p=0.014. There was no difference in the use of PPI between either group. There was a higher level of variability in outcomes reported before the availability of the COS, while the consistency of outcome reporting remained similar. CONCLUSION: The use of a COS in PCI trials is low. This study provides evidence that there still is a lack of awareness of the COS among those who design clinical trials. We also presented the inconsistency and heterogenicity in reporting clinical trial outcomes. Finally, there was a clear lack of PPI utilisation in PCI trials.

A Comprehensive Youth Diabetes Epidemiological Data Set and Web Portal: Resource Development and Case Studies.

BACKGROUND: The prevalence of type 2 diabetes mellitus (DM) and pre-diabetes mellitus (pre-DM) has been increasing among youth in recent decades in the United States, prompting an urgent need for understanding and identifying their associated risk factors. Such efforts, however, have been hindered by the lack of easily accessible youth pre-DM/DM data. OBJECTIVE: We aimed to first build a high-quality, comprehensive epidemiological data set focused on youth pre-DM/DM. Subsequently, we aimed to make these data accessible by creating a user-friendly web portal to share them and the corresponding codes. Through this, we hope to address this significant gap and facilitate youth pre-DM/DM research. METHODS: Building on data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, we cleaned and harmonized hundreds of variables relevant to pre-DM/DM (fasting plasma glucose level ≥100 mg/dL or glycated hemoglobin ≥5.7%) for youth aged 12-19 years (N=15,149). We identified individual factors associated with pre-DM/DM risk using bivariate statistical analyses and predicted pre-DM/DM status using our Ensemble Integration (EI) framework for multidomain machine learning. We then developed a user-friendly web portal named Prediabetes/diabetes in youth Online Dashboard (POND) to share the data and codes. RESULTS: We extracted 95 variables potentially relevant to pre-DM/DM risk organized into 4 domains (sociodemographic, health status, diet, and other lifestyle behaviors). The bivariate analyses identified 27 significant correlates of pre-DM/DM (P<.001, Bonferroni adjusted), including race or ethnicity, health insurance, BMI, added sugar intake, and screen time. Among these factors, 16 factors were also identified based on the EI methodology (Fisher P of overlap=7.06×106). In addition to those, the EI approach identified 11 additional predictive variables, including some known (eg, meat and fruit intake and family income) and less recognized factors (eg, number of rooms in homes). The factors identified in both analyses spanned across all 4 of the domains mentioned. These data and results, as well as other exploratory tools, can be accessed on POND. CONCLUSIONS: Using NHANES data, we built one of the largest public epidemiological data sets for studying youth pre-DM/DM and identified potential risk factors using complementary analytical approaches. Our results align with the multifactorial nature of pre-DM/DM with correlates across several domains. Also, our data-sharing platform, POND, facilitates a wide range of applications to inform future youth pre-DM/DM studies.

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.

Enhancing biostatistics education for medical students in Poland: factors influencing perception and educational recommendations.

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.

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.

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.

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.

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.