Relevant bibliographies by topics / Biostatistics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Biostatistics'

Author: Grafiati
Published: 4 June 2021
Last updated: 22 February 2025

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Journal articles on the topic "Biostatistics"

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Lestari, Endang, Rahayu Rahayu, and Dian Indah Setyorini. "UPAYA PENINGKATAN PENGETAHUAN BIOSTATISTIK MELALUI PELATIHAN BIOSTATISTIK BAGI TENAGA KESEHATAN DI RSUD RA KARTINI JEPARA." JMM (Jurnal Masyarakat Mandiri) 8, no. 2 (April 2, 2024): 2262. http://dx.doi.org/10.31764/jmm.v8i2.22061.

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Abstrak: Kompetensi penelitian bagi tenaga kesehatan sangat penting untuk mendorong gerakan Evidence Based Medicine (EBM), yakni dengan menyediakan bukti bukti terbaik dari penelitian dengan desain terbaik. Kegiatan pengabdian Masyarakat ini ditujukan untuk meningkatkan pengetahuan Biostatistik tenaga Kesehatan RSUD RA Kartini Jepara. Subjek pengabdian ini adalah 24 tenaga Kesehatan di RSUD RA Kartini Jepara. Indikator keberhasilan kegiatan ini adalah peningkatan pengetahuan Biostatistik dan sikap tenaga kesehatan semakin positif terhadap Biostatistik. Kegiatan pengabdian dilakukan dalam bentuk penyuluhan dan praktek olah data serta pemdampingan penyusunan proposal dan pemilihan uji statistic. Data pengetahuan Biostatistik pre dan post pengabdian diolah menggunakan uji T-test berpasangan. Data sikap terhadap Biostatistik pre dan post pelatihan ditampilkan secara deskriptif. Terdapat perbedaan yang signifikan antara nilai pengetahuan statistik sebelum dan sesudah pengabdian (p<0.001). Pasca pelatihan, persepsi tenaga kesehatan terhadap Biostatistik semakin positif. Pengabdian masyarakat dalam bentuk pelatihan biostatistik terhadap tenaga kesehatan RSUD RA Kartini Jepara dapat meningkatkan pengetahuan tenaga kesehatan mengenai biostatistik hingga 42,75% dan dapat meningkatkan persepsi positif terhadap biostatistik.Abstract: Research competency for health professionals is very important to encourage the progress of EBM, particularly by providing the best evidence through studies with the best design. This community service was aimed at increasing the knowledge of biostatistics for health professionals at RA Kartini Hospital, Jepara. In total, 24 health professionals took part the in house training. The indicator of the success of this program was the increase of knowledge of Biostatistics and positive attitude towards Biostatistics among health professionals. This community service program was carried out in the form of lecture and practices of analyzing data utilizing SPSS as well as assistance in preparing proposals and selecting appropriate statistical tests. Data of test score on Biostatistics were collected pre and post service and were analyzed using paired sample T-test test. Data of attitude towards biostatistics pre- and post-training were displayed descriptively. There was a significant difference of biostatistical knowledge scores before and after training (p<0.001. After the training, health professionals' perceptions toward Biostatistics became increasingly positive. Community service in the form of biostatistics training for health professionals at RSUD RA Kartini Jepara could increase the knowledge of biostatistics by up to 42.75% and increase positive perceptions toward biostatistics.
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Cen, Xinyi. "Optimization of Biostatistics Beaching Model from the Perspective of Public Health Emergencies." Journal of Education, Humanities and Social Sciences 23 (December 13, 2023): 378–83. http://dx.doi.org/10.54097/ehss.v23i.12922.

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Using biostatistical methods, simulation in the classroom of emergency events can allow students to solve practical problems in the classroom. This paper discusses the application of flipped classroom in promoting the reform of biostatistics teaching mode, transforming the teaching mode of biostatistics, and improving the teaching quality of biostatistics from the perspective of public health events. Practice has proved that this experiment has achieved good teaching effect.
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Trukhacheva, Nina, Nikolay Pupyrev, Ylia Alyabieva, and Svetlana Tschernysheva. "Teaching Biostatistics to Medical Students of the Altai State Medical University." Politehnika 3, no. 1 (December 3, 2019): 7–11. http://dx.doi.org/10.36978/cte.3.1.1.

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The current research presents some principles and settings in teaching biostatistics. The purpose of study is to enhance the teaching of biostatistics in Russian medical schools and overcome some problems by applying new approaches and innovation techniques. The research included the questionnaire of students, teachers and postgraduates of the Altai State Medical University. There were applied different approaches and studying by means of learning system MOODLЕ with differentiated courses in biostatistics. Categorical data were analyzed using the chi-square test and a P-value less than 0.05 was considered statistically significant. The findings showed some new approaches and methods in education to improve the biostatistical competence of medical students. The changing in content of biostatistical course would increase students’ motivation if it were maximum approximated to the real medical cases. The analysis of the results proved that some approaches are more effective for teaching biostatistics. They allow students to study according to their personal educational goals and paths.
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Kehinde Josephine Olowe, Ngozi Linda Edoh, Stephane Jean Christophe Zouo, and Jeremiah Olamijuwon. "Theoretical perspectives on biostatistics and its multifaceted applications in global health studies." International Journal of Applied Research in Social Sciences 6, no. 11 (November 20, 2024): 2791–806. http://dx.doi.org/10.51594/ijarss.v6i11.1726.

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Biostatistics plays a crucial role in global health studies by providing the tools necessary for analyzing complex health data and informing evidence-based public health decisions. This theoretical review explores the foundations of biostatistics and its diverse applications in the context of global health. At its core, biostatistics utilizes mathematical and statistical principles to analyze health data, enabling researchers to identify patterns, determine risk factors, assess interventions, and predict health outcomes across populations. Key theoretical concepts, including probability theory, statistical inference, and regression models, form the backbone of biostatistical analysis and are integral to understanding the dynamic relationships between health determinants and outcomes. The review highlights the various applications of biostatistics in global health, focusing on areas such as disease surveillance, epidemiology, and public health intervention evaluation. Biostatistical techniques are essential for analyzing epidemiological data, monitoring disease trends, and evaluating the effectiveness of health policies and interventions on a global scale. Furthermore, advanced statistical methods, including machine learning and Bayesian approaches, are becoming increasingly important in global health research. These methods allow for the analysis of large-scale data, predictive modeling, and more precise risk assessments, particularly in the context of emerging diseases, genetic research, and environmental health studies. Despite its strengths, biostatistics faces challenges in global health, including issues related to data quality, ethical considerations, and cross-cultural variations in health outcomes. Addressing these challenges is critical for ensuring accurate and equitable health assessments. Looking forward, biostatistics continues to evolve with innovations in data science, big data analytics, and real-time health data collection, making it an indispensable tool for improving global health outcomes and supporting health policies. This review emphasizes the theoretical underpinnings of biostatistics while highlighting its transformative potential in global health research and practice. Keywords: Theoretical Perspectives, Biostatistics, Multifaceted Applications, Global Health Studies.
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Shi, Chongwei. "Research on the Application of Computer Technology in Biostatistics." Journal of Computing and Electronic Information Management 14, no. 3 (October 22, 2024): 12–15. http://dx.doi.org/10.54097/73xk3226.

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This paper explores the application of computer technology in biostatistics, focusing on the technological advancements and their impact on data management, data processing and analysis, and data visualization. With the development of computer technology, data storage and management methods have significantly improved, particularly with the introduction of database systems and big data technologies, which have greatly enhanced the efficiency and accuracy of handling biostatistical data. Additionally, the application of machine learning and artificial intelligence has made biostatistical data analysis more intelligent and automated, providing new tools for identifying and predicting complex data patterns. Furthermore, advanced data visualization techniques have enhanced the interpretability of statistical results through clear graphical presentations, promoting the application of biostatistics. Through the analysis of specific case studies, this paper demonstrates the practical effects and challenges of computer technology in improving biostatistical research capabilities. Finally, the paper anticipates the future impact of emerging computing technologies, such as quantum computing, on biostatistics and suggests future research directions and recommendations.
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Collins, Andrew P., Max McCall, Joshua Cassinat, Alison Grise, Jonathan Schwartzman, John Kelly, and Benjamin C. Service. "Performance of Orthopaedic Shoulder and Elbow Surgeons on a Biostatistical Knowledge Examination." Advances in Orthopedics 2023 (September 11, 2023): 1–8. http://dx.doi.org/10.1155/2023/8840263.

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Background. The objective of this study is to evaluate the biostatistical interpretation abilities of fellowship trained orthopaedic surgeons. Methods. A cross-sectional survey was administered to orthopaedic surgeon members of the American Shoulder and Elbow Surgeons (ASES), assessing orthopaedic surgeon attitudes towards biostatistics, confidence in understanding biostatistics, and ability to interpret biostatistical measures on a multiple-choice test. Results. A 4.5% response rate was achieved with 55 complete survey responses. The mean percent correct was 55.2%. Higher knowledge test scores were associated with younger age and fewer years since board exam completion ( p ≤ 0.001 ). Greater average number of publications per year correlated with superior statistical interpretation ( p = 0.009 ). Respondents with higher self-reported confidence were more likely to accurately interpret results ( p ≤ 0.017 ). Of the respondents, 93% reported frequently using statistics to form medical opinions, 98% answered that statistical competency is important in the practice of orthopaedic surgery, and 80% were eager to continue learning biostatistics. Conclusions. It is concerning that fellowship-trained shoulder and elbow surgeons, many of whom frequently publish or are reviewing scientific literature for publication, are scoring 55.2% correctly on average on this biostatistical knowledge examination. Surgeons that are further from formal statistical knowledge training are more likely to have lower biostatistical knowledge test scores. Respondents who published at the highest rate were associated with higher scores. Continuing medical education in biostatistics may be beneficial for maintaining statistical knowledge utilised in the current literature.
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Batra, Manu, Mudit Gupta, Subha Soumya Dany, and Prashant Rajput. "Perception of Dental Professionals towards Biostatistics." International Scholarly Research Notices 2014 (October 29, 2014): 1–6. http://dx.doi.org/10.1155/2014/291807.

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Biostatistics is becoming an integral part of dental sciences. Awareness regarding the subject is not thoroughly assessed in the field of dentistry. So the study was conducted to assess dental professionals’ knowledge, attitude, and perception toward biostatistics at an academic dental institution. An anonymous cross-sectional questionnaire survey was conducted among all the faculty and postgraduate students of two dental colleges in Moradabad, Uttar Pradesh. The responses were assessed on 5-point likert scale. The survey response rate was 73.71%. Two-thirds of respondents believed biostatistics to be a difficult subject and at the same time half of them did not consider it to be more difficult than other subjects in dentistry. Females were less competent than males in applying biostatistical skills which was found to be statistically significant. Results suggested that dentists with research or academics as an adjunct to their clinical practice had better command over the subject. The current study shows that there is lack of command over the subject of biostatistics among dental professionals although they were aware of its importance in dentistry. There is a need of changing the training pattern of biostatistics for dental professionals which would make them confident enough to apply biostatistics in their clinical practice.
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Vasudevan, Senthilvel. "Teaching of subject Biostatistics Its Applications and Research Methodology to Undergraduate Pharmacy Students in Central Region of Saudi Arabia." International Journal of Medical Sciences and Nursing Research 3, no. 2 (June 30, 2023): 14–16. http://dx.doi.org/10.55349/ijmsnr.2023321416.

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Background: Biostatistics means statistical tools have been used in the study of medical/biomedical sciences. It is also called Medical Biostatistics/Medical Statistics. It is an important subject in medical, nursing, pharmacy, Bioallied Health Sciences, and other biology related specialties. In this article, I briefly explained my teaching and research experiences related to Biostatistics to the College of Pharmacy (COP) students. Materials and Methods: In this paper I have discussed and explored all the steps related to teaching Biostatistics, its applications, and research methods and its implications to the College of Pharmacy students in Saudi Arabia. Online classes was conducted at the time of Covid-19 pandemic time of the year 2020 – 2021 and 2021 – 2022. Results: I trained COP students in all aspects of Biostatistical tools, their applications, research methods, and other research steps fully. That’s why the Statistical thinking is a crucial part of the studies in pharmacy/medical and biomedical areas. COP students followed my instructions and advice in a good way, and they benefited from my teaching and research methods and its steps throughout their graduation study. Conclusion: I have concluded that teaching of the subject biostatistics and its applications are essential and important to all science and medical specialty students all over the world. Saudi Government must take a necessary step to implement/initiate Biostatistics departments/unit in all colleges separately. It will be very useful to their pharmacist/pharmacy to educate the patients, safe and high-quality care, to control the drugs effect/adherence among patients successfully and to achieve the best high-quality results of patients’ health. Key Words: biostatistics, teaching, research methodology, college of pharmacy, Saudi Arabia
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Chirwa, Tobias F., Zvifadzo Matsena Zingoni, Pascalia Munyewende, Samuel O. Manda, Henry Mwambi, Ngianga-Bakwin Kandala, Samson Kinyanjui, et al. "Developing excellence in biostatistics leadership, training and science in Africa: How the Sub-Saharan Africa Consortium for Advanced Biostatistics (SSACAB) training unites expertise to deliver excellence." AAS Open Research 3 (October 5, 2020): 51. http://dx.doi.org/10.12688/aasopenres.13144.1.

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The increase in health research in sub-Saharan Africa (SSA) has generated large amounts of data and led to a high demand for biostatisticians to analyse these data locally and quickly. Donor-funded initiatives exist to address the dearth in statistical capacity, but few initiatives have been led by African institutions. The Sub-Saharan African Consortium for Advanced Biostatistics (SSACAB) aims to improve biostatistical capacity in Africa according to the needs identified by African institutions, through (collaborative) masters and doctoral training in biostatistics. We describe the SSACAB Consortium, which comprises 11 universities and four research institutions- supported by four European universities. SSACAB builds on existing resources to strengthen biostatistics for health research with a focus on supporting biostatisticians to become research leaders; building a critical mass of biostatisticians, and networking institutions and biostatisticians across SSA. In 2015 only four institutions had established Masters programmes in biostatistics and SSACAB supported the remaining institutions to develop Masters programmes. In 2019 the University of the Witwatersrand became the first African institution to gain Royal Statistical Society accreditation for a Biostatistics MSc programme. A total of 150 fellows have been awarded scholarships to date of which 123 are Masters fellowships (41 female) of which with 58 have already graduated. Graduates have been employed in African academic (19) and research (15) institutions and 10 have enrolled for PhD studies. A total of 27 (10 female) PhD fellowships have been awarded; 4 of them are due to graduate by 2020. To date, SSACAB Masters and PhD students have published 17 and 31 peer-reviewed articles, respectively. SSACAB has also facilitated well-attended conferences, face-to-face and online short courses. Pooling the limited biostatistics resources in SSA, and combining with co-funding from external partners is an effective strategy for the development and teaching of advanced biostatistics methods, supervision and mentoring of PhD candidates.
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Chirwa, Tobias F., Zvifadzo Matsena Zingoni, Pascalia Munyewende, Samuel O. Manda, Henry Mwambi, Ngianga-Bakwin Kandala, Samson Kinyanjui, et al. "Developing excellence in biostatistics leadership, training and science in Africa: How the Sub-Saharan Africa Consortium for Advanced Biostatistics (SSACAB) training unites expertise to deliver excellence." AAS Open Research 3 (December 22, 2020): 51. http://dx.doi.org/10.12688/aasopenres.13144.2.

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The increase in health research in sub-Saharan Africa (SSA) has led to a high demand for biostatisticians to develop study designs, contribute and apply statistical methods in data analyses. Initiatives exist to address the dearth in statistical capacity and lack of local biostatisticians in SSA health projects. The Sub-Saharan African Consortium for Advanced Biostatistics (SSACAB) led by African institutions was initiated to improve biostatistical capacity according to the needs identified by African institutions, through collaborative masters and doctoral training in biostatistics. SACCAB has created a critical mass of biostatisticians and a network of institutions over the last five years and has strengthened biostatistics resources and capacity for health research studies in SSA. SSACAB comprises 11 universities and four research institutions which are supported by four European universities. In 2015, only four universities had established Masters programmes in biostatistics and SSACAB supported the remaining seven to develop Masters programmes. In 2019 the University of the Witwatersrand became the first African institution to gain Royal Statistical Society accreditation for a Biostatistics Masters programme. A total of 150 fellows have been awarded scholarships to date of which 123 are Masters fellowships (41 female) of whom 58 have already graduated. Graduates have been employed in African academic (19) and research (15) institutions and 10 have enrolled for PhD studies. A total of 27 (10 female) PhD fellowships have been awarded; 4 of them are due to graduate by 2020. To date, SSACAB Masters and PhD students have published 17 and 31 peer-reviewed articles, respectively. SSACAB has also facilitated well-attended conferences, face-to-face and online short courses. Pooling of limited biostatistics resources in SSA combined with co-funding from external partners has shown to be an effective strategy for the development and teaching of advanced biostatistics methods, supervision and mentoring of PhD candidates.
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Dissertations / Theses on the topic "Biostatistics"

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Silva, Tiago André dos Santos. "Internship report in biostatistics." Master's thesis, Universidade de Aveiro, 2011. http://hdl.handle.net/10773/6154.

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Mestrado em Biomedicina Farmacêutica
Este relatório descreve a minha experiência de 9 meses enquanto estagiário na Eurotrials, Consultores Científicos, uma Empresa especializada em investigação clínica e consultoria científica. Este estágio desenrolou-se em duas vertentes: formação multidisciplinar e monodisciplinar. A formação multidisciplinar envolveu alguma forma de participação activa em diferentes departamentos desta Empresa, com o objectivo de obter uma perspectiva alargada do processo multidisciplinar inerente ao desenvolvimento clínico de produtos de saúde. A formação monodisciplinar concentrou-se na área de estatística médica, sendo realizada no departamento de Bioestatística da Empresa, com o objectivo de obter conhecimentos práticos de aplicação da estatística à investigação em saúde, implicando também a interiorização de conceitos estatísticos fundamentais. Este estágio permitiu-me compreender de forma mais aprofundada o trabalho multidisciplinar necessário para a realização adequada de um projecto de investigação clínica. Permitiu-me também não só adquirir conhecimentos importantes de análise estatística, mas também compreender, de forma mais clara, o papel da estatística na investigação clínica, como ferramenta essencial no planeamento do estudo, análise e interpretação dos dados obtidos.
This report describes my experience of 9 months as an intern at Eurotrials, Scientific Consultants, a company devoted to clinical research and scientific consulting. This internship developed in two aspects: multidisciplinary and monodosciplinary training. Multidisciplinary training involved active participation in different departments of this Company, with the objective of obtaining a broad perspective on the multidisciplinary process of the clinical development of medical products. Monodisciplinary training was focused in medical statistics, being carried out in the Biostatistics department of the Company. The objective was to obtain practical knowledge for the application of statistics in health sciences. This implied the learning of fundamental statistical concepts. This internship allowed me to understand, in depth, the multidisciplinary work necessary for an adequate performance of a clinical research project. It also allowed me to acquire valuable knowledge in statistical analysis, as well as to clearly understand the role of statistics in clinical research, as an essential tool in study planning, analysis and interpretation of data obtained.
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Puza, Borek Dalibor. "Aspects of Bayesian biostatistics." Thesis, Canberra, ACT : The Australian National University, 1994. http://hdl.handle.net/1885/140911.

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Li, Yin. "Application of logistic regression in biostatistics." Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=68201.

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The primary objective of this paper is a focused introduction to the logistic regression model and its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariates. The approach we will take is to develop the model from a regression analysis point of view. Also in this paper, an estimator of the common odds ratio in one-to-one matched case-control studies is proposed. The connection between this estimator and the James-Stein estimating procedure is highlighted through the argument of estimating functions. Comparisons are made between this estimator, the conditional maximum likelihood estimator, and the estimator ignoring the matching.
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SBROLLINI, AGNESE. "Biostatistics of Cardiac Signals: Theory & Applications." Doctoral thesis, Università Politecnica delle Marche, 2019. http://hdl.handle.net/11566/263514.

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L’obiettivo della bioingegneria è lo studio dei fenomeni delle scienze della vita. La statistica è un eccellente strumento per la modellazione, l’analisi, la caratterizzazione e l’interpretazione di questi fenomeni. Scopo di questa tesi di dottorato è quello di combinare le principali tecniche statistiche con l'elaborazione dei segnali cardiaci. L'importanza delle statistiche nella bioingegneria cardiaca può essere compresa attraverso la loro applicazione; quindi, sono state presentate quattro applicazioni reali. La prima applicazione è l'Adaptive Thresholding Identification Algorithm (AThrIA), nato per identificare le onde P elettrocardiografiche. AThrIA è l'esempio perfetto di quanto la preelaborazione statistica possa essere importante nella pratica clinica cardiaca. La seconda applicazione è CTG Analyzer, un'interfaccia che estrae automaticamente le caratteristiche cliniche cardiotocografiche. In tal caso, la statistica diventa lo strumento per valutarne la correttezza delle caratteristiche estratte. La terza applicazione è eCTG, un software per digitalizzare i segnali cardiotocografici. Combinando l’analisi delle distribuzioni e le tecniche di classificazione, eCTG è un importante esempio dell’utilizzo della statistica nell'elaborazione di immagini e segnali. Infine, la quarta applicazione è la creazione di classificatori per l’elettrocardiografia seriale basati su deep learning. Questi nuovi e innovativi classificatori rappresentano un esempio di come la classificazione statistica supporta la diagnosi clinica. In conclusione, questa tesi di dottorato sottolinea l'importanza della statistica nella bioingegneria dei segnali cardiaci. Considerando i risultati e il loro significato clinico, la combinazione di bioingegneria cardiaca e statistica è uno strumento valido per supportare la ricerca scientifica. Legati allo stesso scopo, tali scienze sono in grado di caratterizzare i fenomeni delle scienze della vita, diventando una scienza unica, la biostatistica.
Aim of bioengineering is to investigate phenomena of life sciences. Considering that statistic is an excellent tool for modeling, analyzing, characterizing and interpreting phenomena, aim of this doctoral thesis is to merge the major biostatistical techniques and the bioengineering processing of cardiac signals. The importance of statistics in cardiac bioengineering can be deeply understand through its application; thus, four real applications were presented. The first is the Adaptive Thresholding Identification Algorithm (AThrIA), born to identify/characterize electrocardiographic P waves. AThrIA is the perfect example of how much statistical preprocessing can be important in cardiac clinical practice. The second application is CTG Analyzer, an interface that automatically extracts cardiotocographic clinical features. About CTG Analyzer feature extraction, biostatistics is a fundamental instrument to evaluate its correctness. The third application is eCTG, a software to digitalize cardiotocographic signals from images, using a statistical pixel clustering procedure. Combining distributions analysis and classification, eCTG is an important example of statistics in image/signal processing. Finally, the fourth application is the creation of deep-learning serial ECG classifiers, specific neural networks to detect cardiac emerging pathology. Based on serial electrocardiography, these new and innovative classifiers represent samples of the real importance of classification in supporting clinical diagnosis. In conclusion, this doctoral thesis underlines the importance of statistic in bioengineering of cardiac signals. Considering the results and their clinical meaning, the combination of cardiac bioengineering and statistics is a valid instrument to support the scientific research. Linked by the same aim, they are able to quantitative/qualitative characterize the phenomena of life sciences, becoming a single science, biostatistics.
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Nam, In-Sun. "Contributions to the theory and practice of Biostatistics." Thesis, Queensland University of Technology, 2000. https://eprints.qut.edu.au/105691/1/T%28S%29%20737%20Contributions%20to%20the%20theory%20and%20practice%20of%20biostatistics.pdf.

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Biostatistics is the application of statistical concepts and methods to medicine and the biological sciences. The objectives of this work are twofold: 1. To develop suitable multivariate meta analysis methods which will contribute to the theory of biostatistics and to apply them to a new dataset on passive smoking and health outcomes (asthma and lower respiratory disease) in children, a topic of current interest (NHMRC report). 2. To provide case studies and details of statistical advice given to clinicians at the Prince Charles Hospital with the aim to conduct statistically sound trials and analyses. The first section of this work is devoted to the development of methodology for multivariate meta analysis; First I define meta analysis and discuss the initial motivation for the work. I then explain how the example dataset was constructed. Moreover, the conventional meta analysis methods are described and applied to the example data. Finally I introduce two Bayesian multivariate meta analysis models and these are also applied to the example dataset and the results are compared to the previous ones. The distinctive benefit of the multivariate analysis is that it is possible to evaluate the overall effect of passive smoking to childhood respiratory health as well as its effects on single events. The author's experience as a statistical consultant in a number of areas at the Prince Charles Hospital is explained in the second section. It also summarises common areas of concern arising from this experience and gives advice on general approaches to statistical analyses.
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Barcella, William. "Covariate dependent random measures with applications in biostatistics." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/10037679/.

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In Bayesian nonparametrics, the specification of suitable (for practical purposes) stochastic processes whose realisations are discrete probability measures plays a crucial role. Recently, real world applications have motivated the extension of these stochastic processes to incorporate covariate information in the realisations with the aim of constructing infinite mixture models having weights and/or component-specific parameters which depend on covariates. This work presents four different modelling strategies motivated by practical problems involving stochastic processes over covariate dependent random measures. After presenting the main concepts in Bayesian nonparametrics and reviewing relevant literature, we develop two Bayesian models which are extensions of augmented response mixture models. In particular, we construct a semi-parametric non-linear regression model for zero-inflated discrete distributions and propose techniques to perform variable selection in cluster-specific regression models. The third contribution presents a generalisation of Dirichlet Process for random probability measures to include covariate information via Beta regression. Properties of this new stochastic process are discussed and two illustrations are presented for dealing with spatially correlated observations and grouped longitudinal data. The last part of the thesis proposes a modelling strategy for time-evolving correlated binary vectors, which relies on latent variables. The distribution of these latent variables is assumed to be a convolution of Gaussian kernels with covariate dependent random probability measures. These four modelling strategies are motivated by datasets that come from medical studies involving lower urinary tract symptoms and acute lymphoblastic leukaemia as well as from publicly available data about primary schools evaluations in London.
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Shi, Jing. "Biostatistics and bioinformatics methods for analysis of pathways and gene expression /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Karlsson, Andreas. "Estimation and Inference for Quantile Regression of Longitudinal Data : With Applications in Biostatistics." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7186.

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Zhao, Sihai. "Survival Analysis with High-Dimensional c\Covariates, with Applications to Cancer Genomics." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10245.

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Recent technological advances have given cancer researchers the ability to gather vast amounts of genetic and genomic data from individual patients. These offer tantalizing possibilities for, for example, basic cancer biology, tailored therapies, and personalized risk predictions. At the same time, they have also introduced many analytical difficulties that cannot be properly addressed with current statistical procedures, because the number of genomic covariates in these datasets is often larger than the sample size. In this dissertation we study methods for addressing this so-called high-dimensional issue when genomic data are used to analyze time-to-event outcomes, so common to clinical cancer studies. In Chapter 1, we propose a regularization method for sparse estimation for estimating equations. Our method can be used even when the number of covariates exceeds the number of samples, and can be implemented using well-studied algorithms from the non-linear constrained optimization literature. Furthermore, for certain estimating equations and certain regularizers, including the lasso and group lasso, we prove a finite-sample probability bound on the accuracy of our estimator. However, it is well-known that these types of regularization methods can achieve better performance if a quick and simple procedure is first used to reduce the number of covariates. In Chapter 2, we propose and theoretically justify a principled method for reducing dimensionality in the analysis of censored data by selecting only the important covariates. Our procedure involves a tuning parameter that has a simple interpretation as the desired false positive rate of this selection. Similar types of model-based screening methods have also been proposed, but only for a few specific models. Model-free screening methods have also recently been studied, but can have lower power to detect important covariates. In Chapter 3 we propose a screening procedure that can be used with any model that can be fit using estimating equations, and provide unified results on its finite-sample screening performance. We thus generalize many recently proposed model-based and model-free screening procedures. We also propose an iterative version of our method and show that it is closely related to a recently studied boosting method for estimating equations.
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Snavely, Anna Catherine. "Multivariate Data Analysis with Applications to Cancer." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10371.

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Multivariate data is common in a wide range of settings. As data structures become increasingly complex, additional statistical tools are required to perform proper analyses. In this dissertation we develop and evaluate methods for the analysis of multivariate data generated from cancer trials. In the first chapter we consider the analysis of clustered survival data that can arise from multicenter clinical trials. In particular, we review and compare marginal and conditional models numerically through simulations and discuss model selection techniques. A multicenter clinical trial of children with acute lymphoblastic leukemia is used to illustrate the findings. The second and third chapters both address the setting where multiple outcomes are collected when the outcome of interest cannot be measured directly. A head and neck cancer trial in which multiple outcomes were collected to measure dysphagia was the particular motivation for this part of the dissertation. Specifically, in the second chapter we propose a semiparametric latent variable transformation model that incorporates measurable outcomes of mixed types, including censored outcomes. This method extends traditional approaches by allowing the relationship between the measurable outcomes and latent variable to be unspecified, rendering more robust inference. Using this approach we can directly estimate the treatment (or other covariate) effect on the unobserved latent variable, enhancing interpretation. In the third chapter, the basic model from the second chapter is maintained, but additional parametric assumptions are made. This model still has the advantages of allowing for censored measurable outcomes and being able to estimate a treatment effect on the latent variable, but has the added advantage of good performance in a small data set. Together the methods proposed in the second and third chapters provide a comprehensive approach for the analysis of complex multiple outcomes data.
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Books on the topic "Biostatistics"

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MacNeill, Ian B., Gary J. Umphrey, Allan Donner, and V. Krishna Jandhyala, eds. Biostatistics. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4794-8.

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Van Belle, Gerald, Lloyd D. Fisher, Patrick J. Heagerty, and Thomas Lumley. Biostatistics. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/0471602396.

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Williams, Brian. Biostatistics. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-6906-4.

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1931-, MacNeill Ian B., Umphrey Gary J. 1953-, and Joshi V. M, eds. Biostatistics. Dordrecht: D. Reidel, 1987.

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Le, Chap T. Introductory Biostatistics. New York: John Wiley & Sons, Ltd., 2003.

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Källén, Anders. Understanding Biostatistics. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119992677.

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Lesaffre, Emmanuel, and Andrew B. Lawson. Bayesian Biostatistics. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119942412.

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Oliveira, A. Gouveia, ed. Biostatistics Decoded. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118670767.

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A, Berry Donald, and Stangl Dalene K. 1956-, eds. Bayesian biostatistics. New York: M. Dekker, 1996.

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Indrayan, Abhaya. Medical biostatistics. New York: Marcel Dekker, 2000.

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Book chapters on the topic "Biostatistics"

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Mosteller, Frederick. "Biostatistics." In The Pleasures of Statistics, 289–91. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-77956-0_20.

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Kunkov, Sergey. "Biostatistics." In Prepare for the Pediatric Emergency Medicine Board Examination, 683–710. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-28372-8_34.

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Rahbar, Elaheh, Sapan S. Desai, Eric Mowatt-Larssen, and Mohammad Hossein Rahbar. "Biostatistics." In Phlebology, Vein Surgery and Ultrasonography, 355–68. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01812-6_25.

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Wilson, Susan R. "Biostatistics." In International Encyclopedia of Statistical Science, 158–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_147.

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Kumar Singh, Bikesh, and G. R. Sinha. "Biostatistics." In Machine Learning in Healthcare, 1–21. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003097808-1.

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Khasim, S. M., K. Thammasiri, S. Rama Rao, and M. Rahamtulla. "Biostatistics." In Plant Techniques, 137–71. London: CRC Press, 2024. http://dx.doi.org/10.1201/9781003503682-11.

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de Paz, Carlos Chavez, and Allen Murga. "Biostatistics." In The Vascular Surgery In-Training Examination Review (VSITE), 527–39. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24121-5_28.

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Wimann, Sawyer Cimaroli, and Shahidul Islam. "Biostatistics." In The ABSITE Blueprints, 677–83. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32643-1_21.

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Hans, Sachinder Singh. "Biostatistics." In Self-Assessment for Vascular and Endovascular Specialists, 357–63. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003389897-21.

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Husar, Peter, and Gabriel Gašpar. "Biostatistics." In Electrical Biosignals in Biomedical Engineering, 355–440. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-67998-2_6.

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Conference papers on the topic "Biostatistics"

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Lucia Marçal Mazza Sundefeld, Maria. "A successful experience of database employment." In Statistics and the Internet. International Association for Statistical Education, 2003. http://dx.doi.org/10.52041/srap.03314.

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Dental School students usually take the biostatistics course for granted. In Brazil, some Schools of Dentistry do not offer Biostatistics as a course in the curriculum, but at the Dental School of Araçatuba, São Paulo State, Brazil, Biostatistics is a three-hour-per-week course. Objective: To motivate the students to learn Biostatistics by using the databases of different dental fields. Methodology: To teach Biostatistics using the databases of different fields of dentistry. After 2 years using dentistry databases, an opinion survey was applied to all professors and students from the 2nd through the last year. After 3 years, an opinion survey was applied to all professors. Result and Discussion: 88.8% of the students and 94.1% of the professors felt that it is very important to learn Biostatistics in a Dental School. In the last two years, the students have been asking for extra-class training positions at the Biostatistics section. Conclusion: This methodology makes learning easier and more interesting.
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Prihanti, Gita Sekar. "EVALUATING BIOSTATISTICAL COMPETENCIES FOR MEDICAL STUDENT: THE BENEFITS OF A BIOSTATISTICS CENTRE DEVELOPMENT." In International Conference on Public Health. The International Institute of Knowledge Management (TIIKM), 2018. http://dx.doi.org/10.17501/icoph.2017.3212.

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van Strik, Roel. "Teaching Biostatistics to medical Students." In Proceedings of the First Scientific Meeting of the IASE. International Association for Statistical Education, 1993. http://dx.doi.org/10.52041/srap.93209.

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Sundefeld, Maria Lucia Marçal Mazza, Amália Moreno, and Márcia Regina Espírito Santo. "Assessing the inclusion of biostatistics in schools of dentistry in brazil." In Assessing Student leaning in Statistics. International Association for Statistical Education, 2007. http://dx.doi.org/10.52041/srap.07404.

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The National Institute of Study and Research of the Ministry of Education in Brazil states that there are 169 Schools of Dentistry in 2006. At the School of Dentistry of Araçatuba, UNESP, Brazil, this Biostatistics discipline is included in the course with 90 credit hours a year. An interrelationship with other professors has been developed for some years using database of different dental fields. The aim of this research is to get acquainted with the teaching of Biostatics at schools of dentistry in Brazil. The methodology applied was to send the questionnaire to all the coordinators of the courses. As for the result of 76 questionnaires answered, 45 stated the inclusion of Biostatistics in their program studies. Only in 3 schools Biostatistics is taught all over the year in “61 to 90” hours. The conclusion is that the teaching of Biostatistics must be incentivated in all schools of dentistry in Brazil.
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Cruz, João Pedro. "PROBLEM BASED LEARNING IN A BIOSTATISTICS COURSE." In 12th annual International Conference of Education, Research and Innovation. IATED, 2019. http://dx.doi.org/10.21125/iceri.2019.2330.

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Octavia, Eva Nur, and Pandu Riono. "Effectivity of National Health Insurance on Maternal Health in Developing Countries: A Systematic Review." In THE 7th INTERNATIONAL CONFERENCE ON PUBLIC HEALTH 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph-fp.04.17.

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Cuijun Zhao and Guoquan Jiang. "An application multimedia to the teaching of biostatistics." In 2010 2nd International Conference on Education Technology and Computer (ICETC). IEEE, 2010. http://dx.doi.org/10.1109/icetc.2010.5529418.

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Zhang, Jing, Yunrong Yan, Zhongliang Wang, Xuefeng Wang, and Chunyan Shen. "Excellent Resource Sharing Course Construction on Aquatic Biostatistics." In 2016 International Forum on Management, Education and Information Technology Application. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/ifmeita-16.2016.69.

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Zhu, Shu-Ren, Hui-Hui Hu, and Wei-Qin Li. "Extraction and Application of the Signature Biostatistics Features." In First International Conference on Innovative Computing, Information and Control. IEEE, 2006. http://dx.doi.org/10.1109/icicic.2006.451.

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Xiao, Xiaonan. "Exploration of Biostatistics Research in the New Century." In Proceedings of the 2018 2nd International Conference on Education Innovation and Social Science (ICEISS 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/iceiss-18.2018.34.

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Reports on the topic "Biostatistics"

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Little, Roderick J. Training Program in Biostatistics for Breast Cancer Research. Fort Belvoir, VA: Defense Technical Information Center, August 1995. http://dx.doi.org/10.21236/ada299342.

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Hovland, P., C. Bischof, D. Spiegelman, and M. Casella. Efficient derivative codes through automatic differentiation and interface contraction: An application in biostatistics. Office of Scientific and Technical Information (OSTI), August 1997. http://dx.doi.org/10.2172/515613.

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Lawson, Andrew. Bayesian Spatial and Spatio-Temporal Modeling in R. Instats Inc., 2024. http://dx.doi.org/10.61700/jsdeeudk51kk31519.

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This workshop provides a comprehensive introduction to advanced techniques for analyzing spatial and spatio-temporal data. While the examples used will be primarily from the health sciences, this four-day hands-on workshop is designed to equip PhD students, professors, and professional researchers with the skills to conduct cutting-edge research in various fields, including Geography, Epidemiology, Public Health, Biostatistics, Ecology, Sociology, and Political Science.
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Oskolkov, Nikolay. Machine Learning for Computational Biology. Instats Inc., 2024. http://dx.doi.org/10.61700/l01vi14ohm8en1490.

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This one-day workshop, led by Nikolay Oskolkov from Lund University, provides a comprehensive introduction to machine learning techniques in computational biology, focusing on both theoretical knowledge and practical coding skills in R and Python. Participants will learn to implement from scratch and optimize algorithms such as neural networks, random forest, k-means clustering, and Markov Chain Monte Carlo (MCMC), making it an essential resource for advancing research in biostatistics, genetics, and data science.
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Muyle, Aline. Analysis of DNA Methylation. Instats Inc., 2024. http://dx.doi.org/10.61700/6ayq8hff26qxn1470.

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This three-day workshop provides comprehensive training in the analysis of DNA methylation, a key epigenetic modification influencing gene expression and controlling the spread of transposable elements in genomes. Led by Dr Aline Muyle, participants will gain theoretical knowledge and practical skills in data preprocessing, statistical analysis, and visualization using RStudio and various bioinformatic software using Bash scripts. The workshop is suited for researchers in Biostatistics, Biology, Genetics, Ecology, Evolution, Bioinformatics, Molecular Biology, and Medical Research.
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‘Adolescent and maternal mental health before during the pandemic’ – In Conversation Dr. Nicky Wright. ACAMH, June 2021. http://dx.doi.org/10.13056/acamh.16165.

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This episode of our JCPP Advances series that focus on the papers and editors featured in the publication is with Dr. Nicola Wright, research associate at the department of Biostatistics and Health Informatics, Kings College London. Nicola discusses her paper 'Interplay between long-term vulnerability and new risk: Young adolescent and maternal mental health immediately before and during the COVID-19 pandemic'.
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Introduction to GraphPad Prism. Instats Inc., 2023. http://dx.doi.org/10.61700/ed95dhmnw0403469.

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This workshop is designed to introduce participants to GraphPad, a comprehensive and user-friendly statistical software widely used in biostatistics for biomedical research. Participants will become familiar with the GraphPad environment, learning basic manipulations such as data entry and import, statistical analyses, and visualization. Emphasis will be placed on different types of tables, and the training will also include a review of basic principles of descriptive and inferential statistics through hands-on examples using the software. For European PhD students, the seminar offers 1 ECTS Equivalent point.
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Introduction to GraphPad Prism. Instats Inc., 2023. http://dx.doi.org/10.61700/aln9mklg7x4qr469.

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This workshop is designed to introduce participants to GraphPad, a comprehensive and user-friendly statistical software widely used in biostatistics for biomedical research. Participants will become familiar with the GraphPad environment, learning basic manipulations such as data entry and import, statistical analyses, and visualization. Emphasis will be placed on different types of tables, and the training will also include a review of basic principles of descriptive and inferential statistics through hands-on examples using the software. For European PhD students, the seminar offers 1 ECTS Equivalent point.
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