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.

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.

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.

The Pacific Innovations, Knowledge, and Opportunities (PIKO) Program: A Data Lifecycle Research Experience.

Pacific evidence-based clinical and translational research is greatly needed. However, there are research challenges that stem from the creation, accessibility, availability, usability, and compliance of data in the Pacific. As a result, there is a growing demand for a complementary approach to the traditional Western research process in clinical and translational research. The data lifecycle is one such approach with a history of use in various other disciplines. It was designed as a data management tool with a set of activities that guide researchers and organizations on the creation, management, usage, and distribution of data. This manuscript describes the data lifecycle and its use by the Biostatistics, Epidemiology, and Research Design core data science team in support of the Center for Pacific Innovations, Knowledge, and Opportunities program.

The impact of biostatistics on hazard characterization using in vitro developmental neurotoxicity assays.

In chemical safety assessment, benchmark concentrations (BMC) and their associated uncertainty are needed for the toxicological evaluation of in vitro data sets. A BMC estimation is derived from concentration-response modelling and results from various statistical decisions, which depend on factors such as experimental design and assay endpoint features. In current data practice, the experimenter is often responsible for the data analysis and therefore relies on statistical software, often without being aware of the software default settings and how they can impact the outputs of data analysis. To provide more insight into how statistical decision-making can influence the outcomes of data analysis and interpretation, we have developed an automated platform that includes statistical methods for BMC estimation, a novel endpoint-specific hazard classification system, and routines that flag data sets that are outside the applicability domain for an automatic data evaluation. We used case studies on a large dataset produced by a developmental neurotoxicity (DNT) in vitro battery (DNT IVB). Here we focused on the BMC and its confidence interval (CI) estimation as well as on final hazard classification. We identified five crucial statistical decisions the experimenter must make during data analysis: choice of replicate averaging, response data normalization, regression modelling, BMC and CI estimation, and choice of benchmark response levels. The insights gained are intended to raise more awareness among experimenters on the importance of statistical decisions and methods but also to demonstrate how important fit-for-purpose, internationally harmonized and accepted data evaluation and analysis procedures are for objective hazard classification.

Biostatistics for gastroenterologists. Part II ̶ Rethinking sample size.

Sample size determination is a critical aspect of medical studies, influencing the reliability and generalizability of research findings. This article explores the importance of sample size in both basic and clinical research. The considerations for sample size differ based on the type of research, whether it is involving humans, animals, or cells. In basic research, a larger sample size is necessary to ensure statistical power and reliable results, enhancing the precision and generalizability of findings. In clinical research, determining an appropriate sample size is crucial for obtaining valid and clinically relevant results, ensuring sufficient statistical power to detect differences between treatment groups or validate intervention efficacy. Reporting sample size calculations accurately and complying with reporting guidelines, such as the CONSORT Statement, is essential for transparent and comprehensive research publications. Consulting a statistician is highly recommended to ensure appropriate sample size determination, enhance scientific rigor, and obtain reliable and clinically relevant findings in medical research.

Bayesian inference for optimal dynamic treatment regimes in practice.

In this work, we examine recently developed methods for Bayesian inference of optimal dynamic treatment regimes (DTRs). DTRs are a set of treatment decision rules aimed at tailoring patient care to patient-specific characteristics, thereby falling within the realm of precision medicine. In this field, researchers seek to tailor therapy with the intention of improving health outcomes; therefore, they are most interested in identifying optimal DTRs. Recent work has developed Bayesian methods for identifying optimal DTRs in a family indexed by ψ via Bayesian dynamic marginal structural models (MSMs) (Rodriguez Duque D, Stephens DA, Moodie EEM, Klein MB. Semiparametric Bayesian inference for dynamic treatment regimes via dynamic regime marginal structural models. Biostatistics; 2022. (In Press)); we review the proposed estimation procedure and illustrate its use via the new BayesDTR R package. Although methods in Rodriguez Duque D, Stephens DA, Moodie EEM, Klein MB. (Semiparametric Bayesian inference for dynamic treatment regimes via dynamic regime marginal structural models. Biostatistics; 2022. (In Press)) can estimate optimal DTRs well, they may lead to biased estimators when the model for the expected outcome if everyone in a population were to follow a given treatment strategy, known as a value function, is misspecified or when a grid search for the optimum is employed. We describe recent work that uses a Gaussian process ( G P ) prior on the value function as a means to robustly identify optimal DTRs (Rodriguez Duque D, Stephens DA, Moodie EEM. Estimation of optimal dynamic treatment regimes using Gaussian processes; 2022. Available from: https://doi.org/10.48550/arXiv.2105.12259). We demonstrate how a G P approach may be implemented with the BayesDTR package and contrast it with other value-search approaches to identifying optimal DTRs. We use data from an HIV therapeutic trial in order to illustrate a standard analysis with these methods, using both the original observed trial data and an additional simulated component to showcase a longitudinal (two-stage DTR) analysis.

The role of biostatistics in the response to COVID-19: a Belgian and international perspective.

In this commentary to Dattner et al. (Israel J Health Policy Res. 11:22, 2022), we highlight similarities and differences in the role that biostatistics and biostatisticians have been playing in the COVID-19 response in Belgium and Israel. We bring out implications and opportunities for our field and for science. We argue that biostatistics has an important place in the multidisciplinary COVID-19 response, in terms of research, policy advice, and science and public communication. In Belgium, biostatisticians located in various institutes, collaborated with epidemiologists, vaccinologists, infectiologists, immunologists, social scientists, and government policy makers to provide rapid and science-informed policy advice. Biostatisticians, who can easily be mobilized to work together in pandemic response, also played a role in public communication.

Biostatistics for gastroenterologists. Part I.

Statistical tests are the foundation on which data analysis for all types of clinical, basic and epidemiological research relies upon. Clinicians need to understand these tests and the basics of biostatistics to correctly relay the data and results from their research and to understand the results of other scientific publications. The first article in this three-part editorial series aims to present, in a clear way, some essential concepts of biostatistics that can assist the gastroenterologist in understanding scientific research, for an evidence-based clinical practice.

Semiparametric predictive inference for failure data using first-hitting-time threshold regression.

The progression of disease for an individual can be described mathematically as a stochastic process. The individual experiences a failure event when the disease path first reaches or crosses a critical disease level. This happening defines a failure event and a first hitting time or time-to-event, both of which are important in medical contexts. When the context involves explanatory variables then there is usually an interest in incorporating regression structures into the analysis and the methodology known as threshold regression comes into play. To date, most applications of threshold regression have been based on parametric families of stochastic processes. This paper presents a semiparametric form of threshold regression that requires the stochastic process to have only one key property, namely, stationary independent increments. As this property is frequently encountered in real applications, this model has potential for use in many fields. The mathematical underpinnings of this semiparametric approach for estimation and prediction are described. The basic data element required by the model is a pair of readings representing the observed change in time and the observed change in disease level, arising from either a failure event or survival of the individual to the end of the data record. An extension is presented for applications where the underlying disease process is unobservable but component covariate processes are available to construct a surrogate disease process. Threshold regression, used in combination with a data technique called Markov decomposition, allows the methods to handle longitudinal time-to-event data by uncoupling a longitudinal record into a sequence of single records. Computational aspects of the methods are straightforward. An array of simulation experiments that verify computational feasibility and statistical inference are reported in an online supplement. Case applications based on longitudinal observational data from The Osteoarthritis Initiative (OAI) study are presented to demonstrate the methodology and its practical use.