Module Specifications.
Current Academic Year 2024 - 2025
All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).
As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.
Date posted: September 2024
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Description This course covers the basics for statistical methods used most often to analyse quantitative data collected in medical and biomedical informatics studies, including clinical trials and epidemiologic studies. Emphasis is placed on assessing data quality, understanding how to select an overall approach to analysis, and presenting, visualising, and interpreting the results of statistical analysis. Students will learn to use R to effectively display and analyse data collected in biomedical research studies. Upon completion of this course, students will: 1) recognize the importance of data quality and how to ensure data quality before undertaking analysis; 2) conduct statistical analysis of data from an array of designs used commonly in biomedical studies; 3) interpret statistical analysis of biomedical data from a variety of study designs. | |||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Exhibit a critical awareness by the ability to recognize problems with data quality before undertaking analysis. 2. For an array of designs used commonly in biomedical studies, demonstrate a systematic understanding of knowledge by conducting an analysis of the data using R and interpreting the results. 3. Exercise initiative by demonstrating the ability to conduct a power analysis and estimating sample size requirements for planned studies. 4. Critically evaluate error rates and the positive and negative predictive values of an assay before applying the assay. 5. Develop predictive and explanatory models using an assortment of regression methods, and to compare and assess such models using a range of techniques such as likelihood and Receiver Operating Characteristic (ROC) curve analysis. 6. Analyse predicted survival curves and the factors which influence probability of survival. | |||||||||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and Learning Activities
Applied Biostatistics for Medicine and InformaticsIntroduction and Concepts; Correlation, 2-group tests and ANOVA; Linear Regression; Multiple Linear Regression and Regression Diagnostics; Logistic Regression; Survival Analysis; Power and Sample Size. | |||||||||||||||||||||||||||||||||||||||||||
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||