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.
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Details
Academic unit: Bond Business School Subject code: ACSC13-307 Subject title: Survival Analysis Subject level: Undergraduate Semester/Year: May 2024 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Forum: x12 (Total hours: 24) - Forum
- Computer Lab: x12 (Total hours: 24) - Computer Lab
- 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. -
Resources
Prescribed resources: No Prescribed resources.
After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.iLearn@Bond & Email: iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class 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 www.bond.edu.au
| Academic unit: | Bond Business School |
|---|---|
| Subject code: | ACSC13-307 |
| Subject title: | Survival Analysis |
| Subject level: | Undergraduate |
| Semester/Year: | May 2024 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
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| 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. |
| Prescribed resources: | No Prescribed resources. After enrolment, students can check the Books and Tools area in iLearn for the full Resource List. |
|---|---|
| iLearn@Bond & Email: | iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class 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 www.bond.edu.au |
Enrolment requirements
| Requisites: |
Pre-requisites:Co-requisites:There are no co-requisites |
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| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.
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| Restrictions: |
Nil |
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.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Demonstrate an understanding of censoring and lifetime random variables.
- Estimate and analyse a variety of survival models, including parametric, non-parametric and proportional hazard models.
- Demonstrate an understanding of the benefits of machine learning techniques in survival analysis.
- Estimate and analyse machine learning models in the presence of censored data.
- Use a statistical package frequently used by practitioners for survival analysis.
Generative Artificial Intelligence in Assessment
The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.
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Assessment details
Type Task % Timing* Outcomes assessed Computer-Aided Examination (Open) Comprehensive Final Examination 40.00% Final Examination Period 1,2,3,4,5 Computer-Aided Examination (Open) Mid Semester Exam 35.00% Week 7 (Mid-Semester Examination Period) 1,2,5 Technical Document§ Group assignment 1 comprising a selection of questions, many applied, designed to test the relevant learning outcomes. 10.00% 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. 15.00% Week 11 1,2,3,4,5 - § 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.
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Assessment criteria
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.
| Type | Task | % | Timing* | Outcomes assessed |
|---|---|---|---|---|
| Computer-Aided Examination (Open) | Comprehensive Final Examination | 40.00% | Final Examination Period | 1,2,3,4,5 |
| Computer-Aided Examination (Open) | Mid Semester Exam | 35.00% | Week 7 (Mid-Semester Examination Period) | 1,2,5 |
| Technical Document§ | Group assignment 1 comprising a selection of questions, many applied, designed to test the relevant learning outcomes. | 10.00% | 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. | 15.00% | Week 11 | 1,2,3,4,5 |
- § 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 iLearn@Bond 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 the Lead Educator grants an extension. 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.
Academic Integrity
Bond University‘s Student Code of Conduct Policy , Student Charter, Academic Integrity Policy and our Graduate Attributes guide expectations regarding student behaviour, their rights and responsibilities. Information on these topics can be found on our Academic Integrity webpage recognising that academic integrity involves demonstrating the principles of integrity (honesty, fairness, trust, professionalism, courage, responsibility, and respect) in words and actions across all aspects of academic endeavour.
Staff are required to report suspected misconduct. This includes all types of plagiarism, cheating, collusion, fabrication or falsification of data/content or other misconduct relating to assessment such as the falsification of medical certificates for assessment extensions. The longer term personal, social and financial consequences of misconduct can be severe, so please ask for help if you are unsure.
If your work is subject to an inquiry, you will be given an opportunity to respond and appropriate support will be provided. Academic work under inquiry will not be marked until the process has concluded. Penalties for misconduct include a warning, reduced grade, a requirement to repeat the assessment, suspension or expulsion from the University.
Feedback on assessment
Feedback on assessment will be provided to students according to the requirements of the Assessment Procedure Schedule A - Assessment Communication Procedure.
Whilst in most cases feedback should be provided within two weeks of the assessment submission due date, the Procedure should be checked if the assessment is linked to others or if the subject is a non-standard (e.g., intensive) subject.
Accessibility and Inclusion Support
Support is available to students where a physical, mental or neurological condition exists that would impact the student’s capacity to complete studies, exams or assessment tasks. For effective support, special requirement needs should be arranged with the University in advance of or at the start of each semester, or, for acute conditions, as soon as practicable after the condition arises. Reasonable adjustments are not guaranteed where applications are submitted late in the semester (for example, when lodged just prior to critical assessment and examination dates).
As outlined in the Accessibility and Inclusion Policy, to qualify for support, students must meet certain criteria. Students are also required to meet with the Accessibility and Inclusion Advisor who will ensure that reasonable adjustments are afforded to qualifying students.
For more information and to apply online, visit BondAbility.
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
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Concept of censoring, survival/hazard and lifetime random variable (inc. expectation and variance)
Key definition and notation for survival analysis are introduced. Calculations with lifetime random variables are then presented.
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Parametric estimation with censored data
Skewed and lifetime distributions including gamma, Weibull, log-normal, Pareto, Gompertz and Makeham. Also, censoring mechanisms and their effect on outcome distributions.
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Non-parametric models for censored data
Kaplan-Meier and Nelson-Aalen estimators.
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Proportional Hazard models / semi-parametric models for censored data
The concepts of Proportional Hazards and Partial Likelihood are first introduced. The Cox Proportional Hazards Model is then discussed in detail.
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Markov Models for Survival Analysis
Two-state models for survival and health data are presented. Generalisations of these to multi-state models are then discussed.
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Binomial and Poisson Methods for Survival Analysis
This topic covers the Binomial and Poisson methods for survival estimation. The concepts of Exposed to Risk and Central Exposed to Risk are introduced.
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Smoothing/Graduation and Hypothesis Testing for Survival Analysis
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.
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Modern data analytics for smoothing/graduation
Modern data analytics approaches to smoothing are presented and discussed, including the key task of balancing fidelity to the data with smoothness.
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Machine learning models for censored data
Machine learning in general is introduced, including specific models designed to handle censored data.