Module Catalogue 2026/27

HSC3102 : Topics in Medical Statistics and Health Data Science

HSC3102 : Topics in Medical Statistics and Health Data Science

  • Offered for Year: 2026/27
  • Module Leader(s): Dr Nan Lin
  • Co-Module Leader: Professor James Wason
  • Owning School: Population Health Sciences
  • Teaching Location: Newcastle City Campus
Semesters

Your programme is made up of credits, the total differs on programme to programme.

Semester 2 Credit Value: 10
ECTS Credits: 5.0
European Credit Transfer System
Pre-requisite

Modules you must have done previously to study this module

Code Title
MAS2901Statistical Inference
Pre Requisite Comment

MAS2903 and MAS2906 are also desirable rather than essential.

Co-Requisite

Modules you need to take at the same time

Co Requisite Comment

N/A

Aims

To develop a broad understanding of a range of topics in medical statistics, and in health and medical research. To acquire skills in analysing and interpreting health data, with a focus on the design and analysis of medical studies. The module will cover topics such as observational studies and causal inference. Students will learn to apply statistical methods to real-world medical data, enhancing their ability to contribute to advancements in medical research and public health.

Outline Of Syllabus

This module will introduce students to a range of topics in medical statistics and health data science. The anticipated syllabus is given below, although there may be changes to reflect recent developments.

Counterfactual outcome, causal effects, the perfect doctor, ignorability, Simpson’s Paradox, confounding, Directed Acyclic Graphs, measurements in medical statistics and inferences, biases in observational studies and remedies

Learning Outcomes

Intended Knowledge Outcomes

At the end of the module it is expected that a student will be able to:

1. Outline the key statistical ideas underpinning the analysis of medical and health data.

2. Apply statistical methods to real-world health data scenarios.

3. Describe the advantages and disadvantages of different statistical methods used in medical research.

Intended Skill Outcomes

At the end of the module a student will be able to:

1. Identify appropriate statistical methods for a wide variety of medical and health data.

2. Implement these analyses using relevant software tools like R.

3. Evaluate different statistical methods, apply these methods to real-world health data and effectively communicate their findings.

Students will develop skills across the cognitive domain (Bloom’s taxonomy, 2001 revised edition):
remember, understand, apply, analyse, evaluate and create.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture51:005:00Problem classes
Guided Independent StudyAssessment preparation and completion12:002:00Unseen exam
Scheduled Learning And Teaching ActivitiesLecture21:002:00Revision lectures
Scheduled Learning And Teaching ActivitiesLecture201:0020:00Formal lectures
Guided Independent StudyAssessment preparation and completion24:008:00Completion of in course assessments
Guided Independent StudyIndependent study251:0025:00Background reading on lectured content
Guided Independent StudyIndependent study131:0013:00Revision for unseen exam
Guided Independent StudyIndependent study221:0022:00Preparation time for lectures
Guided Independent StudyIndependent study21:303:00Review of coursework
Total100:00
Jointly Taught With
Code Title
HSC8102Advanced Topics in Medical Statistics and Health Data Science
Teaching Rationale And Relationship

Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem classes are used to help develop the students’ abilities at applying the theory to solving problems.

The teaching methods are appropriate to allow students to develop a wide range of skills. From understanding basic concepts and facts to higher-order thinking.

Reading Lists

Assessment Methods

The format of resits will be determined by the Board of Examiners

Exams
Description Length Semester When Set Percentage Comment
Written Examination1202A80Written exam comprising a Section A and a Section B
Exam Pairings
Module Code Module Title Semester Comment
Advanced Topics in Medical Statistics and Health Data Science2N/A
Other Assessment
Description Semester When Set Percentage Comment
Prob solv exercises2M20Coursework 2. Up to 6 page typeset report based upon a set assignment comprising open-ended questions
Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Description Semester When Set Comment
Prob solv exercises2MCoursework 1. 40 minute class test, conducted during one of the timetabled one hour lecture slots
Assessment Rationale And Relationship

A substantial formal unseen examination is appropriate for the assessment of the material in this module. The format of the examination will enable students to reliably demonstrate their own knowledge, understanding and application of learning outcomes.

Examination problems may require a synthesis of concepts and strategies from different sections, while they may have more than one way for solution. The examination time allows the students to test different strategies, work out examples and gather evidence for deciding on an effective strategy, while carefully articulating their ideas and explicitly citing the theory they are using.

The coursework assignments allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; the summative assessment has a secondary formative purpose as well as its primary summative purpose.

Timetable

Past Exam Papers

General Notes

N/A

Welcome to Newcastle University Module Catalogue

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You may have some queries about the modules available to you. Your school office will be able to signpost you to someone who will support you with any queries.

Disclaimer

The information contained within the Module Catalogue relates to the 2026 academic year.

In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.

Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, staffing changes, and student feedback. Module information for the 2027/28 entry will be published here in early-April 2027. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.