Tutorial on Biostatistics: Sample Size and Power Calculation for Ophthalmic Studies With Correlated Binary Eye Outcomes.

Purpose: To describe and demonstrate sample size and power calculation for ophthalmic studies with a binary outcome from one or both eyes. Methods: We describe sample size and power calculation for four commonly used eye designs: (1) one-eye design or person-design: one eye per subject or outcome is at person-level; (2) paired design: two eyes per subject and two eyes are in different treatment groups; (3) two-eye design: two eyes per subject and both eyes are in the same treatment group; and (4) mixture design: mixture of one eye and two eyes per subject. For each design, we demonstrate sample size and power calculations in real ophthalmic studies. Results: Using formulas and commercial or free statistical packages including SAS, STATA, R, and PS, we calculated sample size and power. We demonstrated that different statistical packages require different parameters and provide similar, yet not identical, results. We emphasize that studies using data from two eyes of a subject need to account for the intereye correlation for appropriate sample size and power calculations. We demonstrate the gain in efficiency in designs that include two eyes of a subject compared to one-eye designs. Conclusions: Ophthalmic studies use different eye designs that include one or both eyes in the same or different treatment groups. Appropriate sample size and power calculations depend on the eye design and should account for intereye correlation when two eyes from some or all subjects are included in a study. Calculations can be executed using formulas and commercial or free statistical packages.

Biostatistical Aspects of Whole Genome Sequencing Studies: Preprocessing and Quality Control.

Rapid advances in high-throughput DNA sequencing technologies have enabled large-scale whole genome sequencing (WGS) studies. Before performing association analysis between phenotypes and genotypes, preprocessing and quality control (QC) of the raw sequence data need to be performed. Because many biostatisticians have not been working with WGS data so far, we first sketch Illumina's short-read sequencing technology. Second, we explain the general preprocessing pipeline for WGS studies. Third, we provide an overview of important QC metrics, which are applied to WGS data: on the raw data, after mapping and alignment, after variant calling, and after multisample variant calling. Fourth, we illustrate the QC with the data from the GENEtic SequencIng Study Hamburg-Davos (GENESIS-HD), a study involving more than 9000 human whole genomes. All samples were sequenced on an Illumina NovaSeq 6000 with an average coverage of 35× using a PCR-free protocol. For QC, one genome in a bottle (GIAB) trio was sequenced in four replicates, and one GIAB sample was successfully sequenced 70 times in different runs. Fifth, we provide empirical data on the compression of raw data using the DRAGEN original read archive (ORA). The most important quality metrics in the application were genetic similarity, sample cross-contamination, deviations from the expected Het/Hom ratio, relatedness, and coverage. The compression ratio of the raw files using DRAGEN ORA was 5.6:1, and compression time was linear by genome coverage. In summary, the preprocessing, joint calling, and QC of large WGS studies are feasible within a reasonable time, and efficient QC procedures are readily available.

Learning from disability studies to introduce the role of the individual to naturalistic accounts of disease.

Disability studies have been successfully focusing on individuals' lived experiences, the personalization of goals, and the constitution of the individual in defining disease and restructuring public understandings of disability. Although they had a strong influence in the policy making and medical modeling of disease, their framework has not been translated to traditional naturalistic accounts of disease. I will argue that, using new developments in evolutionary biology (Extended Evolutionary Synthesis [EES] about questions of proper function) and behavioral ecology (Niche conformance and construction about the questions of reference classes in biostatistics accounts), the main elements of the framework of disability studies can be used to represent life histories at the conceptual level of the two main "non-normative" accounts of disease. I chose these accounts since they are related to medicine in a more descriptive way. The success of the practical aspects of disability studies this way will be communicated without causing injustice to the individual since they will represent the individuality of the patient in two main naturalistic accounts of disease: the biostatistical account and the evolutionary functional account. Although most accounts criticizing the concept of disease as value-laden do not supply a positive element, disability studies can supply a good point for descriptive extension of the concept through inclusion of epistemic agency.

Wakefield's Harm-Based Critique of the Biostatistical Theory.

Jerome Wakefield criticizes my biostatistical analysis of the pathological-as statistically subnormal biological part-functional ability relative to species, sex, and age-for its lack of a harm clause. He first charges me with ignoring two general distinctions: biological versus medical pathology, and disease of a part versus disease of a whole organism. He then offers 10 counterexamples that, he says, are harmless dysfunctions but not medical disorders. Wakefield ends by arguing that we need a harm clause to explain American psychiatry's 1973 decision to declassify homosexuality. I reply, first, that his two distinctions are philosophic fantasies alien to medical usage, invented only to save his own harmful-dysfunction analysis (HDA) from a host of obvious counterexamples. In any case, they do not coincide with the harmless/harmful distinction. In reality, medicine admits countless chronic diseases that are, contrary to Wakefield, subclinical for most of their course, as well as many kinds of typically harmless skin pathology. As for his 10 counterexamples, no medical source he cites describes them as he does. I argue that none of his examples contradicts the biostatistical analysis: all either are not part-dysfunctions (situs inversus, incompetent sperm, normal-flora infection) or are indeed classified as medical disorders (donated kidney, Typhoid Mary's carrier status, latent tuberculosis or HIV, cherry angiomas). And if Wakefield's HDA fits psychiatry, the fact that it does not fit medicine casts doubt on psychiatry's status as a medical specialty.

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.

Rolling the DICE (Design, Interpret, Compute, Estimate): Interactive Learning of Biostatistics With Simulations.

Despite the increasing relevance of statistics in health sciences, teaching styles in higher education are remarkably similar across disciplines: lectures covering the theory and methods, followed by application and computer exercises in given data sets. This often leads to challenges for students in comprehending fundamental statistical concepts essential for medical research. To address these challenges, we propose an engaging learning approach-DICE (design, interpret, compute, estimate)-aimed at enhancing the learning experience of statistics in public health and epidemiology. In introducing DICE, we guide readers through a practical example. Students will work in small groups to plan, generate, analyze, interpret, and communicate their own scientific investigation with simulations. With a focus on fundamental statistical concepts such as sampling variability, error probabilities, and the construction of statistical models, DICE offers a promising approach to learning how to combine substantive medical knowledge and statistical concepts. The materials in this paper, including the computer code, can be readily used as a hands-on tool for both teachers and students.

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.

Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation.

We present the motivation, experience, and learnings from a data challenge conducted at a large pharmaceutical corporation on the topic of subgroup identification. The data challenge aimed at exploring approaches to subgroup identification for future clinical trials. To mimic a realistic setting, participants had access to 4 Phase III clinical trials to derive a subgroup and predict its treatment effect on a future study not accessible to challenge participants. A total of 30 teams registered for the challenge with around 100 participants, primarily from Biostatistics organization. We outline the motivation for running the challenge, the challenge rules, and logistics. Finally, we present the results of the challenge, the participant feedback as well as the learnings. We also present our view on the implications of the results on exploratory analyses related to treatment effect heterogeneity.

Poor statistical reporting: do we have a reason for concern? A narrative review and recommendations.

PURPOSE OF REVIEW: The aim of the review conducted was to present recent articles indicating the need to implement statistical recommendations in the daily work of biomedical journals. RECENT FINDINGS: The most recent literature shows an unchanged percentage of journals using specialized statistical review over 20 years. The problems of finding statistical reviewers, the impractical way in which biostatistics is taught and the nonimplementation of published statistical recommendations contribute to the fact that a small percentage of accepted manuscripts contain correctly performed analysis. The statistical recommendations published for authors and editorial board members in recent years contain important advice, but more emphasis should be placed on their practical and rigorous implementation. If this is not the case, we will additionally continue to experience low reproducibility of the research. SUMMARY: There is a low level of statistical reporting these days. Recommendations related to the statistical review of submitted manuscripts should be followed more rigorously.

Phases of methodological research in biostatistics-Building the evidence base for new methods.

Although new biostatistical methods are published at a very high rate, many of these developments are not trustworthy enough to be adopted by the scientific community. We propose a framework to think about how a piece of methodological work contributes to the evidence base for a method. Similar to the well-known phases of clinical research in drug development, we propose to define four phases of methodological research. These four phases cover (I) proposing a new methodological idea while providing, for example, logical reasoning or proofs, (II) providing empirical evidence, first in a narrow target setting, then (III) in an extended range of settings and for various outcomes, accompanied by appropriate application examples, and (IV) investigations that establish a method as sufficiently well-understood to know when it is preferred over others and when it is not; that is, its pitfalls. We suggest basic definitions of the four phases to provoke thought and discussion rather than devising an unambiguous classification of studies into phases. Too many methodological developments finish before phase III/IV, but we give two examples with references. Our concept rebalances the emphasis to studies in phases III and IV, that is, carefully planned method comparison studies and studies that explore the empirical properties of existing methods in a wider range of problems.

An R Shiny Application (SDOH) for Predictive Modeling Using Regional Social Determinants of Health Survey Responses.

Social determinants of health (SDoH) surveys are data sets that provide useful health-related information about individuals and communities. This study aims to develop a user-friendly web application that allows clinicians to get a predictive insight into the social needs of their patients before their in-patient visits using SDoH survey data to provide an improved and personalized service. The study used a longitudinal survey that consisted of 108,563 patient responses to 12 questions. Questions were designed to have a binary outcome as the response and the patient's most recent responses for each of these questions were modeled independently by incorporating explanatory variables. Multiple classification and regression techniques were used, including logistic regression, Bayesian generalized linear model, extreme gradient boosting, gradient boosting, neural networks, and random forests. Based on the area under the curve values, gradient boosting models provided the highest precision values. Finally, the models were incorporated into an R Shiny application, enabling users to predict and compare the impact of SDoH on patients' lives. The tool is freely hosted online by the University of Kansas Medical Center's Department of Biostatistics and Data Science. The supporting materials for the application are publicly accessible on GitHub.

How to check a simulation study.

Simulation studies are powerful tools in epidemiology and biostatistics, but they can be hard to conduct successfully. Sometimes unexpected results are obtained. We offer advice on how to check a simulation study when this occurs, and how to design and conduct the study to give results that are easier to check. Simulation studies should be designed to include some settings in which answers are already known. They should be coded in stages, with data-generating mechanisms checked before simulated data are analysed. Results should be explored carefully, with scatterplots of standard error estimates against point estimates surprisingly powerful tools. Failed estimation and outlying estimates should be identified and dealt with by changing data-generating mechanisms or coding realistic hybrid analysis procedures. Finally, we give a series of ideas that have been useful to us in the past for checking unexpected results. Following our advice may help to prevent errors and to improve the quality of published simulation studies.

Marshall Joffe's Contributions to Causal Inference, Biostatistics, and Epidemiology.

We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall's research and their significance for causal inference but also offer a portrayal of Marshall's personal accomplishments and character. Our discussion of Marshall's research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall's unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall's research. While Marshall's impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.

PGS-Depot: a comprehensive resource for polygenic scores constructed by summary statistics based methods.

Polygenic score (PGS) is an important tool for the genetic prediction of complex traits. However, there are currently no resources providing comprehensive PGSs computed from published summary statistics, and it is difficult to implement and run different PGS methods due to the complexity of their pipelines and parameter settings. To address these issues, we introduce a new resource called PGS-Depot containing the most comprehensive set of publicly available disease-related GWAS summary statistics. PGS-Depot includes 5585 high quality summary statistics (1933 quantitative and 3652 binary trait statistics) curated from 1564 traits in European and East Asian populations. A standardized best-practice pipeline is used to implement 11 summary statistics-based PGS methods, each with different model assumptions and estimation procedures. The prediction performance of each method can be compared for both in- and cross-ancestry populations, and users can also submit their own summary statistics to obtain custom PGS with the available methods. Other features include searching for PGSs by trait name, publication, cohort information, population, or the MeSH ontology tree and searching for trait descriptions with the experimental factor ontology (EFO). All scores, SNP effect sizes and summary statistics can be downloaded via FTP. PGS-Depot is freely available at http://www.pgsdepot.net.

Biostatistics for gastroenterologists. Part III - Mastering meta-analysis for beginners.

Meta-analysis combines data from multiple independent studies to derive robust conclusions. This article provides an insightful guide to conducting meta-analyses with a particular focus on gastroenterology studies. The process of conducting a meta-analysis involves several systematic steps. Firstly, formulating a clear research question using the PICO format. Subsequently, a comprehensive literature search is conducted using databases like PubMed and Embase, followed by the establishment of inclusion and exclusion criteria to select eligible studies. Data extraction, quality assessment, and statistical analysis using suitable software are then undertaken. Key aspects of performing the meta-analysis include choosing between fixed-effects and random-effects models based on heterogeneity levels among studies. Calculating summary effect sizes and visualizing results through forest plots aid in interpreting outcomes effectively. This article emphasizes the significance of meta-analysis in evidence synthesis, illustrating its role in enhancing decision-making processes by providing more comprehensive and reliable conclusions. It quantifies relationships between variables, resolves conflicting findings, and increases statistical power by aggregating data. Meta-analysis stands as an indispensable tool in research and decision-making, requiring collaboration with statisticians for methodological rigor and accuracy.

Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data.

With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate high-density longitudinal data and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this article, we propose a group-based method to cluster a collection of multivariate high-density longitudinal data via a Bayesian mixture of smoothing splines. Our method assumes each multivariate high-density longitudinal trajectory is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy, which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates.

Fundamento y generalidades de la construcción de modelos estadísticos multinivel en el ámbito de la investigación en salud / Construction of multilevel statistical models in health research: Foundations and generalities

Este trabajo tiene como objetivo presentar una mirada global de la aplicabilidad de los modelos de análisis multinivel en el ámbito de la investigación sanitaria. Ofrece información sobre los fundamentos teóricos, metodológicos y estadísticos y, además, menciona los pasos básicos para la construcción de estos modelos, y da ejemplos de su uso, según la estructura jerárquica de los datos. Cabe resaltar que, antes de utilizar estos modelos, se requiere contar con un soporte teórico sobre la necesidad de uso y una valoración estadística que dé cuenta del porcentaje de varianza explicada por el efecto de agrupación de las observaciones. Los requisitos para llevar a cabo este tipo de análisis dependen de condiciones especiales como el tipo de variables, la cantidad de unidades por nivel o el tipo de estructura jerárquica. Se concluye que los modelos de análisis multinivel son una herramienta útil para lograr la integración de información, dadas la complejidad de las relaciones y las interacciones que determinan la mayoría de las condiciones de salud, incluida la pérdida de independencia entre las unidades de observación.

This topic review aims to present a global vision of multilevel analysis models' applicability to health research, explaining its theoretical, methodological, and statistical foundations. We describe the basic steps to build these models and examples of their application according to the data hierarchical structure. It ir worth noticing that before using these models, researchers must have a rationale for needing them, and a statistical evaluation accounting for the variance percentage explained by the observations grouping effect. The requirements to conduct this type of analysis depends on special conditions such as the type of variables, the number of units per level, or the type of hierarchical structure. We conclude that multilevel analysis models are a useful tool to integrate information, considering the complexity of the relationships and interactions involved in most health conditions, including the loss of independence between observation units.