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Ordak M. Enhancing biostatistics education for medical students in Poland: factors influencing perception and educational recommendations. BMC MEDICAL EDUCATION 2024; 24:428. [PMID: 38649993 PMCID: PMC11034022 DOI: 10.1186/s12909-024-05389-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 04/03/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND A number of recommendations for the teaching of biostatistics have been published to date, however, student opinion on them has not yet been studied. For this reason, the aim of the manuscript was to find out the opinions of medical students at universities in Poland on two forms of teaching biostatistics, namely traditional and practical, as well as to indicate, on the basis of the results obtained, the related educational recommendations. METHODS The study involved a group of 527 students studying at seven medical faculties in Poland, who were asked to imagine two different courses. The traditional form of teaching biostatistics was based on the standard teaching scheme of running a test from memory in a statistical package, while the practical one involved reading an article in which a particular test was applied and then applying it based on the instruction provided. Other aspects related to the teaching of the subject were assessed. RESULTS According to the students of each course, the practical form of teaching biostatistics reduces the stress level associated with teaching and the student exam (p < 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.
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Affiliation(s)
- Michal Ordak
- Department of Pharmacotherapy and Pharmaceutical Care, Faculty of Pharmacy, Medical University of Warsaw, 1 Banacha Street, 02-097, Warsaw, Poland.
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Durham EL, Ghanem K, Stier AJ, Cardenas-Iniguez C, Reimann GE, Jeong HJ, Dupont RM, Dong X, Moore TM, Berman MG, Lahey BB, Bzdok D, Kaczkurkin AN. Multivariate analytical approaches for investigating brain-behavior relationships. Front Neurosci 2023; 17:1175690. [PMID: 37583413 PMCID: PMC10423877 DOI: 10.3389/fnins.2023.1175690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 07/13/2023] [Indexed: 08/17/2023] Open
Abstract
Background Many studies of brain-behavior relationships rely on univariate approaches where each variable of interest is tested independently, which does not allow for the simultaneous investigation of multiple correlated variables. Alternatively, multivariate approaches allow for examining relationships between psychopathology and neural substrates simultaneously. There are multiple multivariate methods to choose from that each have assumptions which can affect the results; however, many studies employ one method without a clear justification for its selection. Additionally, there are few studies illustrating how differences between methods manifest in examining brain-behavior relationships. The purpose of this study was to exemplify how the choice of multivariate approach can change brain-behavior interpretations. Method We used data from 9,027 9- to 10-year-old children from the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) to examine brain-behavior relationships with three commonly used multivariate approaches: canonical correlation analysis (CCA), partial least squares correlation (PLSC), and partial least squares regression (PLSR). We examined the associations between psychopathology dimensions including general psychopathology, attention-deficit/hyperactivity symptoms, conduct problems, and internalizing symptoms with regional brain volumes. Results The results of CCA, PLSC, and PLSR showed both consistencies and differences in the relationship between psychopathology symptoms and brain structure. The leading significant component yielded by each method demonstrated similar patterns of associations between regional brain volumes and psychopathology symptoms. However, the additional significant components yielded by each method demonstrated differential brain-behavior patterns that were not consistent across methods. Conclusion Here we show that CCA, PLSC, and PLSR yield slightly different interpretations regarding the relationship between child psychopathology and brain volume. In demonstrating the divergence between these approaches, we exemplify the importance of carefully considering the method's underlying assumptions when choosing a multivariate approach to delineate brain-behavior relationships.
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Affiliation(s)
- E. Leighton Durham
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
| | - Karam Ghanem
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Andrew J. Stier
- Department of Psychology, University of Chicago, Chicago, IL, United States
| | - Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | | | - Hee Jung Jeong
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
| | - Randolph M. Dupont
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
| | - Xiaoyu Dong
- Department of Psychology, Vanderbilt University, Nashville, TN, United States
| | - Tyler M. Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Marc G. Berman
- Department of Psychology, University of Chicago, Chicago, IL, United States
- The University of Chicago Neuroscience Institute, University of Chicago, Chicago, IL, United States
| | - Benjamin B. Lahey
- Department of Health Studies, University of Chicago, Chicago, IL, United States
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States
| | - Danilo Bzdok
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
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Lungeanu D, Petrica A, Lupusoru R, Marza AM, Mederle OA, Timar B. Beyond the Digital Competencies of Medical Students: Concerns over Integrating Data Science Basics into the Medical Curriculum. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15958. [PMID: 36498065 PMCID: PMC9739359 DOI: 10.3390/ijerph192315958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/26/2022] [Accepted: 11/27/2022] [Indexed: 06/17/2023]
Abstract
Introduction. Data science is becoming increasingly prominent in the medical profession, in the face of the COVID-19 pandemic, presenting additional challenges and opportunities for medical education. We retrospectively appraised the existing biomedical informatics (BMI) and biostatistics courses taught to students enrolled in a six-year medical program. Methods. An anonymous cross-sectional survey was conducted among 121 students in their fourth year, with regard to the courses they previously attended, in contrast with the ongoing emergency medicine (EM) course during the first semester of the academic year 2020−2021, when all activities went online. The questionnaire included opinion items about courses and self-assessed knowledge, and questions probing into the respondents’ familiarity with the basics of data science. Results. Appreciation of the EM course was high, with a median (IQR) score of 9 (7−10) on a scale from 1 to 10. The overall scores for the BMI and biostatistics were 7 (5−9) and 8 (5−9), respectively. These latter scores were strongly correlated (Spearman correlation coefficient R = 0.869, p < 0.001). We found no correlation between measured and self-assessed knowledge of data science (R = 0.107, p = 0.246), but the latter was fairly and significantly correlated with the perceived usefulness of the courses. Conclusions. The keystone of this different perception of EM versus data science was the courses’ apparent value to the medical profession. The following conclusions could be drawn: (a) objective assessments of residual knowledge of the basics of data science do not necessarily correlate with the students’ subjective appraisal and opinion of the field or courses; (b) medical students need to see the explicit connection between interdisciplinary or complementary courses and the medical profession; and (c) courses on information technology and data science would better suit a distributed approach across the medical curriculum.
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Affiliation(s)
- Diana Lungeanu
- Center for Modeling Biological Systems and Data Analysis, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
- Department of Functional Sciences, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Alina Petrica
- Department of Surgery, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
- “Pius Brinzeu” Emergency County Clinical Hospital, 300723 Timisoara, Romania
| | - Raluca Lupusoru
- Center for Modeling Biological Systems and Data Analysis, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
- Department of Functional Sciences, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
- “Pius Brinzeu” Emergency County Clinical Hospital, 300723 Timisoara, Romania
| | - Adina Maria Marza
- Department of Surgery, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
- Multidisciplinary Center for Research, Evaluation, Diagnosis and Therapies in Oral Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
- Emergency Municipal Clinical Hospital, 300079 Timisoara, Romania
| | - Ovidiu Alexandru Mederle
- Department of Surgery, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
- Multidisciplinary Center for Research, Evaluation, Diagnosis and Therapies in Oral Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
- Emergency Municipal Clinical Hospital, 300079 Timisoara, Romania
| | - Bogdan Timar
- “Pius Brinzeu” Emergency County Clinical Hospital, 300723 Timisoara, Romania
- Center for Molecular Research in Nephrology and Vascular Disease, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
- Second Department of Internal Medicine, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
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Griessenberger F, Trutschnig W, Junker RR. qad
: An R‐package to detect asymmetric and directed dependence in bivariate samples. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
| | | | - Robert R. Junker
- Department of Environment and Biodiversity University of Salzburg Salzburg Austria
- Evolutionary Ecology of Plants, Department of Biology Philipps‐University Marburg Marburg Germany
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Niso G, Krol LR, Combrisson E, Dubarry AS, Elliott MA, François C, Héjja-Brichard Y, Herbst SK, Jerbi K, Kovic V, Lehongre K, Luck SJ, Mercier M, Mosher JC, Pavlov YG, Puce A, Schettino A, Schön D, Sinnott-Armstrong W, Somon B, Šoškić A, Styles SJ, Tibon R, Vilas MG, van Vliet M, Chaumon M. Good scientific practice in EEG and MEG research: Progress and perspectives. Neuroimage 2022; 257:119056. [PMID: 35283287 PMCID: PMC11236277 DOI: 10.1016/j.neuroimage.2022.119056] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 02/25/2022] [Accepted: 03/01/2022] [Indexed: 11/22/2022] Open
Abstract
Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.
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Affiliation(s)
- Guiomar Niso
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Universidad Politecnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Laurens R Krol
- Neuroadaptive Human-Computer Interaction, Brandenburg University of Technology Cottbus-Senftenberg, Germany
| | - Etienne Combrisson
- Aix-Marseille University, Institut de Neurosciences de la Timone, France
| | | | | | | | - Yseult Héjja-Brichard
- Centre d'Ecologie Fonctionnelle et Evolutive, CNRS, EPHE, IRD, Université Montpellier, Montpellier, France
| | - Sophie K Herbst
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, NeuroSpin center, Université Paris-Saclay, Gif/Yvette, France
| | - Karim Jerbi
- Cognitive and Computational Neuroscience Laboratory, Department of Psychology, University of Montreal, Montreal, QC, Canada; Mila - Quebec Artificial Intelligence Institute, Canada
| | - Vanja Kovic
- Faculty of Philosophy, Laboratory for neurocognition and applied cognition, University of Belgrade, Serbia
| | - Katia Lehongre
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm U 1127, CNRS UMR 7225, APHP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), Paris, France
| | - Steven J Luck
- Center for Mind & Brain, University of California, Davis, CA, USA
| | - Manuel Mercier
- Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, Marseille, France
| | - John C Mosher
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yuri G Pavlov
- University of Tuebingen, Germany; Ural Federal University, Yekaterinburg, Russia
| | - Aina Puce
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Antonio Schettino
- Erasmus University Rotterdam, Rotterdam, the Netherland; Institute for Globally Distributed Open Research and Education (IGDORE), Sweden
| | - Daniele Schön
- Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, Marseille, France
| | | | | | - Anđela Šoškić
- Faculty of Philosophy, Laboratory for neurocognition and applied cognition, University of Belgrade, Serbia; Teacher Education Faculty, University of Belgrade, Serbia
| | - Suzy J Styles
- Psychology, Nanyang Technological University, Singapore; Singapore Institute for Clinical Sciences, A*STAR, Singapore
| | - Roni Tibon
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK; School of Psychology, University of Nottingham, Nottingham, UK
| | - Martina G Vilas
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main, Germany
| | | | - Maximilien Chaumon
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm U 1127, CNRS UMR 7225, APHP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), Paris, France..
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Reynolds PS. Between two stools: preclinical research, reproducibility, and statistical design of experiments. BMC Res Notes 2022; 15:73. [PMID: 35189946 PMCID: PMC8862533 DOI: 10.1186/s13104-022-05965-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 02/08/2022] [Indexed: 11/11/2022] Open
Abstract
Translation of animal-based preclinical research is hampered by poor validity and reproducibility issues. Unfortunately, preclinical research has 'fallen between the stools' of competing study design traditions. Preclinical studies are often characterised by small sample sizes, large variability, and 'problem' data. Although Fisher-type designs with randomisation and blocking are appropriate and have been vigorously promoted, structured statistically-based designs are almost unknown. Traditional analysis methods are commonly misapplied, and basic terminology and principles of inference testing misinterpreted. Problems are compounded by the lack of adequate statistical training for researchers, and failure of statistical educators to account for the unique demands of preclinical research. The solution is a return to the basics: statistical education tailored to non-statistician investigators, with clear communication of statistical concepts, and curricula that address design and data issues specific to preclinical research. Statistics curricula should focus on statistics as process: data sampling and study design before analysis and inference. Properly-designed and analysed experiments are a matter of ethics as much as procedure. Shifting the focus of statistical education from rote hypothesis testing to sound methodology will reduce the numbers of animals wasted in noninformative experiments and increase overall scientific quality and value of published research.
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Affiliation(s)
- Penny S Reynolds
- Department of Anesthesiology, College of Medicine, Department of Small Animal Clinical Sciences, College of Veterinary Medicine, University of Florida, Gainesville, FL, 32610, USA.
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Learning from the past to develop data analysis curricula for the future. PLoS Biol 2021; 19:e3001343. [PMID: 34329307 PMCID: PMC8324210 DOI: 10.1371/journal.pbio.3001343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Problems with statistical analyses and the shift towards big data have prompted many researchers to call for improvements in statistics education. This Primer explores a recent study in PLOS Biology that assesses changes in the use of data analysis techniques over time to determine which skills young scientists might need.
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