Module: Applied Biostatistics for Medicine & Informatics1

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Applied Biostatistics for Medicine & Informatics1
Module Code	STA1004 (ITS: BE521AU)
Faculty	Biotechnology	School	Science & Health
NFQ level	9	Credit Rating	5

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.

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.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	16	Online lectures
Tutorial	6	Tutorials to support the lectures, directed reading material and assignments.
Assignment Completion	78	Completion of an assignment relating to biostatistical analysis of data.
Independent Study	25	Independent study of lecture material and directed reading.
Total Workload: 125

Section Breakdown
CRN	21075	Part of Term	Semester 2
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Emma Finlay	Module Teacher	Denise Harold, Michael Freeley, Paul Cahill

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Short assignment x 6	90%	n/a
Loop Exam	Online exam x 2 (5% each)	10%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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.

Indicative Reading List

Books:
None

Articles:
None

Other Resources

None