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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Applied Biostatistics
Module Code BE521AU
School School of Biotechnology
Module Co-ordinatorSemester 1: Emma Finlay
Semester 2: Emma Finlay
Autumn: Emma Finlay
Module TeachersPaul Cahill
Denise Harold
Emma Finlay
Michael Freeley
NFQ level 9 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

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.

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.

Workload Full-time hours per semester
Type Hours Description
Lecture16Online lectures
Tutorial6Tutorials to support the lectures, directed reading material and assignments.
Assignment Completion78Completion of an assignment relating to biostatistical analysis of data.
Independent Study25Independent study of lecture material and directed reading.
Total Workload: 125

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

Indicative Content and Learning Activities

Applied Biostatistics for Medicine and Informatics
Introduction and Concepts; Correlation, 2-group tests and ANOVA; Linear Regression; Multiple Linear Regression and Regression Diagnostics; Logistic Regression; Survival Analysis; Power and Sample Size.

Assessment Breakdown
Continuous Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentShort assignment x 690%n/a
Loop ExamOnline exam x 2 (5% each)10%n/a
Reassessment Requirement Type
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 1
Indicative Reading List

    Other Resources

    Programme or List of Programmes
    CHPMMaster of Science
    MDPMMSc in Diagnostics & Precision Medicine
    PYPMMaster of Science

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