Module Code - Title:

BM4223 - BIOMEDICAL STATISTICS 2

Year Last Offered:

N/A

Hours Per Week:

Lecture

2

Lab

1

Tutorial

1

Other

0

Private

6

Credits

6

Grading Type:

N

Prerequisite Modules:

BM4202

Rationale and Purpose of the Module:

This module introduces advanced biostatistical methods. It provides students with the tools to apply statistical theory to disease diagnosis and prognosis and a foundation in statistical modelling.

Syllabus:

The syllabus introduces methods to understand and predict health outcomes. The applications focus on disease diagnosis and prognosis. An indicative syllabus is given below: • Relationship between numeric variables - correlation, association and causation, • Correlation analysis in bioinformatics, • Predicting a numeric variable using linear regression, • Risk, relative risk, odds and odd ratios, • Binary logistic regression for disease diagnosis and prognosis, • Regression analysis using open-source statistical software, • Diagnostic tests: accuracy, sensitivity and specificity, positive and negative predictive values, the effect of disease prevalence, receiver operating characteristic curves, area under the curve, likelihood ratio, decision curve analysis.

Learning Outcomes:

Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

On successful completion of this module, students will be able to: • Differentiate between association and causation in epidemiology; • Identify appropriate statistical models for explanation and prediction of health outcomes; • Assess the performance of diagnostic tests; • Apply regression models for explanation and prediction of health outcomes using open-source statistical software; • Critique the presentation of regression analyses.

Affective (Attitudes and Values)

On successful completion of this module, students will be able to: • Discuss the role of statistical modelling as applied to disease diagnosis and prognosis.

Psychomotor (Physical Skills)

On successful completion of this module, students will be able to: N/A

How the Module will be Taught and what will be the Learning Experiences of the Students:

The curriculum has been designed to provide students with a strong experiential learning experience. The module will be taught using a mixture of lectures, practical experience of regression analysis on real-world datasets using open-source statistical software, and case studies in disease diagnosis and prognosis. It will use examples of published medical research to illustrate the application of regression methods and develop the graduate attribute of being curious (critical, knowledgeable and inquisitive). Students will be encouraged to be independent (graduate attribute - courageous) and professionally and ethically responsible (graduate attribute - responsible) in the presentation and interpretation of analyses.)

Research Findings Incorporated in to the Syllabus (If Relevant):

Prime Texts:

A., Petrie, C., Sabin (2019) Medical Statistics at a Glance , Wiley Blackwell

Other Relevant Texts:

Programme(s) in which this Module is Offered:

BMMESUUFA - BACHELOR OF MEDICINE BACHELOR OF SURGERY

Semester(s) Module is Offered:

Autumn

Module Leader:

Ailish.Hannigan@ul.ie