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PHPS40460

Academic Year 2025/2026

Biostatistics II (PHPS40460)

Subject:
Public Health & Population Sci
College:
Health & Agricultural Sciences
School:
Public Hlth, Phys & Sports Sci
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Ricardo Piper Segurado
Trimester:
Spring
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module will build on introductory biostatistics courses. It will concentrate on more advanced techniques employed in the analysis of epidemiological and medical research. The control of confounding using stratification and regression modelling is described in detail and the distinction between effect modifiers and confounders in an analysis is explained. Linear and logistic regression techniques, including the use of dummy variables and modelling interaction effects, is described. Students will be introduced to the analysis of paired or clustered and repeated-measures data, including two-way ANOVA, repeated-measures ANOVA and the concept of a random effect. Particular attention is paid to the analysis of survival (or mortality) in a cohort study, covering such techniques as multiple logistic regression, Kaplan-Meier lifetables and Cox regression.

Emphasis throughout is placed on the understanding of computer output and on the interpretation of results. The module practical element will use the statistical software environment R and the RStudio IDE (although examples and exercises will be provided in other software packages). This will give the student hands-on knowledge of how to run and build linear, logistic or Cox regression models.

About this Module

Learning Outcomes:

At the end of this module students will be able to:
• Appreciate the usefulness and limitations of statistical modelling approaches to data analysis;
• Be able to choose an appropriate model in a given analysis situation, including assessing appropriateness of the mode, and choice of covariates and interaction effects
• Use and interpret statistical software for linear, logistic or Cox regression modelling of their data;
• Present the results of regression models using correct and clear tables and graphs
• Write and evaluate the 'statistical methods' section of a scientific publication.

Note that at the module coordinators discretion a viva voce may be used as an oral assessment for some students.

Indicative Module Content:

- Linear regression
- Logistic regression
- Detection of confounding
- Model-building, including interaction / effect modification
- Time-to event analyses (Kaplan-Meier approach)
- Cox Proportional Hazards Regression
- Clustered and longitudinal analyses with ANOVA or simple mixed-effects models

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

20
Autonomous Student Learning

80
Lectures

24
Total

124

Approaches to Teaching and Learning:
The two-hour classes are a mix of lecture giving the rationale, theory and practical guide to each statistical method or approach, interspersed with active learning micro-sessions, where the students perform guided tasks on their own laptops using the R statistical software environment (RStudio). After each exercise, the students are taken through the output to assist them in interpreting it, and developing skills in the reporting and critical analysis of statistical results.

Additional self-directed reading (textbooks and journal articles), viewing, exercises, and comprehensive study notes supplement the live sessions.

Requirements, Exclusions and Recommendations
Learning Requirements:

Students should have completed the module PHPS40010: Fundamentals of Epidemiology, or equivalent training in study design

Students should have completed the modules PHPS40190: Biostatistics 1 or PHPS41150: Introduction to Biostatistics, or equivalent training in introductory statistics.

Prior to attending this module students should have previous training in the use of the R statistical software environment, or other statistical software. This should have covered data entry and import, setting data types, running simple descriptive statistics, and statistical tests up to one-way ANOVA.

Students must bring their own laptops to lectures.

For more information please contact the module coordinator.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Viva Voce: Optional viva voce may be assigned where an assessment decision reached by other means requires support or clarification Week 15 Pass/Fail Grade Scale No
0
 No
Assignment(Including Essay): Assignment involving simple data analysis and reporting of results in tabular and graphical form. Week 7, Week 8, Week 9 Standard conversion grade scale 40% No
20
 No
Exam (In-person): Written exam on theory and interpretation of statistical models End of trimester
Duration:
2 hr(s)
 Standard conversion grade scale 40% No
80
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

The assignment is given individualised feedback. There is also general in-class feedback on performance and common areas of weakness and strength.

Essential Medical Statistics by Betty Kirkwood and Johnathan Sterne (2nd ed). Print ISBN: 9780865428713; eBook ISBN: 9781444392845. Both available in UCD Library: https://go.exlibris.link/1mxr1ZBz

Danielle Navarro, Learning Statistics with R (v0.6). eBook at https://learningstatisticswithr.com/

Name Role
Dr Ricardo Piper Segurado Lecturer / Co-Lecturer
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