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ACSC13-307: Survival Analysis May 2022 [Standard]

General information

The focus of this subject is analysing the time until an event happens, such as the illness or death of a person, or the failure of a business. The issue of censored data is common in such scenarios and how to handle censored data will be discussed throughout this course. The theory, estimation and application of a variety of survival models for censored data are covered, spanning parametric, semi-parametric and non-parametric models. Machine learning methods suitable for censored data are also covered.


Academic unit:Bond Business School
Subject code:ACSC13-307
Subject title:Survival Analysis
Subject level:Undergraduate
Semester/Year:May 2022
Credit points:10

Delivery & attendance

Delivery mode:


Workload items:
  • Lecture: x12 (Total hours: 24) - Lecture 1
  • Computer Lab: x12 (Total hours: 24) - Computer Lab 2
  • Self-Directed Study Session: x12 (Total hours: 72) - Recommended study time & reviewing materials
Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. As most sessions build on the work covered in the previous one it is difficult to recover if you miss a session.


Prescribed resources: No Prescribed resources. After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.
[email protected] & Email:[email protected] is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.

To access these services, log on to the Student Portal from the Bond University website as

Enrolment requirements

Requisites: ?

Pre-requisites: ?

Co-requisites: ?

There are no co-requisites.

Restrictions: ?


Assurance of learning

Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.

At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.

Program Learning Outcomes (PLOs)

Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.

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Subject Learning Outcomes (SLOs)

On successful completion of this subject the learner will be able to:
  1. Demonstrate an understanding of censoring and lifetime random variables.
  2. Estimate and analyse a variety of survival models, including parametric, non-parametric and proportional hazard models.
  3. Demonstrate an understanding of the benefits of machine learning techniques in survival analysis.
  4. Estimate and analyse machine learning models in the presence of censored data.
  5. Use a statistical package frequently used by practitioners for survival analysis.


Assessment details

TypeTask%Timing*Outcomes assessed
Technical Document § Group assignment 1 comprising a selection of questions, many applied, designed to test the relevant learning outcomes. 10% Week 5 1, 2, 5.
Technical Document § Group assignment 2, same group as the first assignment, comprising a selection of questions, many applied, designed to test the relevant learning outcomes. 15% Week 11 1, 2, 3, 4, 5.
Computer-Aided Examination (Open) Comprehensive Final Examination 40% Final Examination Period 1, 2, 3, 4, 5.
Computer-Aided Examination (Open) Mid Semester Exam 35% Week 7 (Mid-Semester Examination Period) 1, 2, 5.
  • § Indicates group/teamwork-based assessment
  • * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
  • C = Students must reach a level of competency to successfully complete this assessment.

Assessment criteria

High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking.
Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas.
Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above.
Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment.
Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines.

Quality assurance

For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Study information

Submission procedures

Students must check the [email protected] subject site for detailed assessment information and submission procedures.

Policy on late submission and extensions

A late penalty will be applied to all overdue assessment tasks unless an extension is granted by the subject coordinator. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.

Policy on plagiarism

The University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.

Bond University utilises Originality Reporting software to inform academic integrity.

Feedback on assessment

Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.

Disability support

If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.

Additional subject information

A peer-evaluation system will be used in this subject to help determine the individual marks for all group assessments. As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Subject curriculum

Key definition and notation for survival analysis are introduced. Calculations with lifetime random variables are then presented.

Skewed and lifetime distributions including gamma, Weibull, log-normal, Pareto, Gompertz and Makeham. Also, censoring mechanisms and their effect on outcome distributions.

Kaplan-Meier and Nelson-Aalen estimators.

The concepts of Proportional Hazards and Partial Likelihood are first introduced. The Cox Proportional Hazards Model is then discussed in detail.

Two-state models for survival and health data are presented. Generalisations of these to multi-state models are then discussed.

This topic covers the Binomial and Poisson methods for survival estimation. The concepts of Exposed to Risk and Central Exposed to Risk are introduced.

Specific tests for survival models are discussed, including tests for overall goodness of fit, signs, grouping of signs and cumulative deviations. The concept and reason for smoothing/graduating survival probabilities are also discussed.

Modern data analytics approaches to smoothing are presented and discussed, including the key task of balancing fidelity to the data with smoothness.

Machine learning in general is introduced, including specific models designed to handle censored data.

Approved on: Jun 8, 2022. Edition: 2.6