- Class Number 4241
- Term Code 3230
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Dr Le Chang
- Dr Le Chang
- Class Dates
- Class Start Date 21/02/2022
- Class End Date 27/05/2022
- Census Date 31/03/2022
- Last Date to Enrol 28/02/2022
This course introduces survival models and discusses their rationale, their estimation and their application to mortality. Topics covered will include: an introduction to the life table; survival models; estimation procedures for lifetime distributions; statistical models of transfers between multiple states; maximum likelihood estimation of transition intensities for such models; binomial model of mortality including estimation and comparison with multiple state models; exposed to risk and methods for smoothing crude mortality rate data.
Upon successful completion, students will have the knowledge and skills to:
- Explain the concept of survival models;
- Describe the estimation procedures for lifetime distributions;
- Understand and describe statistical models of transfer between multiple states, including processes with single or multiple decrements, and derive relationships between probabilities of transfer and transition intensities;
- Derive maximum likelihood estimators for the transition intensities in models of transfers between states with piecewise constant transition intensities;
- Describe how to estimate transition intensities depending on age, exactly or using the census approximation; and,
- Communicate how to test crude estimates for consistency with a standard table or a set of graduated estimates, and describe the process of graduation.
This course involves an intermediate level of programming with R, including model fitting, graphical analysis and forecasting. In the lectures and computer labs (from week 2 to week 8), we will use real-life datasets to demonstrate the applications of Survival Models in R. Apart from that, additional research articles including journal publications will be provided. Those articles are good examples of how to use appropriate techniques of Survival Models to analyse and solve research questions step-by-step.
Examination Material or equipment
The mid-semester and final exam will be administered online and be open book. Examination materials and conditions will be noticed to all students via Wattle and the examination office.
Lecture notes and other course materials will be updated weekly with the progress of this course on Wattle.
Students will be given feedback in the following forms in this course:
- Tutorial solutions and discussions (online and local tutorials)
- lecturer consultations (online)
ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.
Your final mark for the course will be based on the raw marks allocated for each assignment or examination. However, your final mark may not be the same number as produced by that formula, as marks may be scaled. Any scaling applied will preserve the rank order of raw marks (i.e. if your raw mark exceeds that of another student, then your scaled mark will exceed or equal the scaled mark of that student), and may be either up or down.
Support for Students
The University offers a number of support services for students. Information on these is available online from http://students.anu.edu.au/studentlife/
Technology, Software and Equipment
This course involves intermediate level of programming with R, which is a powerful and free statistical package widely used by industrial professionals and academics.
STAT3032/STAT4072/STAT7042/STAT8003 will be taught jointly. There may be some material which is only relevant to some of these codes. This will be clearly identified during the lecture and/or tutorial. The different cohorts of students will also be treated separately in grading and any scaling that is applied.
|Week/Session||Summary of Activities||Assessment|
|1||Data Types, Introduction to Survival Models|
|2||Introduction to Survival Models|
|4||Kaplan-Meier Estimation||Assignment 1 due Friday of Week 4|
|5||Kaplan-Meier (continued) / Nelson Aalen Estimation / Cox Regression|
|6||Cox Regression (continued)||Mid Semester Exam in either Week 6 or Week 7.|
|7||Two State Models / Multistate Models||Mid Semester Exam in either Week 6 or Week 7|
|8||Multi State Models (continued)|
|9||Mortality Graduation, Smoothing and Forecasting (continued)||Assignment 2 due Friday of Week 9|
|10||Mortality Graduation, Smoothing and Forecasting (continued)|
|11||Mortality Graduating, Smoothing and Forecasting (continued)|
|12||Introduction to Census Method, Exposed to Risk and Rate Intervals||There will be a final exam during the university examination period. More information and instructions regarding the exam will be provided no later than week 10 on Wattle|
Tutorials will be available on campus, live through scheduled zoom sessions, and as pre-recorded videos. Information regarding enrolments for these options will be provided on
Wattle during O-week , prior to the start of the semester.
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|Assignment 1||10 %||18/03/2022||01/04/2022||1-2|
|Mid Semester Exam||25 %||28/03/2022||06/05/2022||1-2|
|Assignment 2||10 %||06/05/2022||20/05/2022||1-2|
|Final Exam||55 %||02/06/2022||30/06/2022||1-6|
* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details
ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:
The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Integrity . In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.
Moderation of Assessment
Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.
Course content delivery will take the form of weekly on-campus lectures (recorded and available via echo360 on Wattle), weekly online (Zoom) consultation, and weekly tutorials, delivered in hybrid format (on campus, live through scheduled Zoom sessions and as pre-recorded videos). Information regarding tutorial enrolments for these options will be provided on Wattle during O-week prior to the start of the semester.
Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule. Centrally scheduled examinations through Examinations, Graduations & Prizes will be timetabled prior to the examination period. Please check ANU Timetabling for further information.
Assessment Task 1
Learning Outcomes: 1-2
The students are expected to complete this assignment individually. This assignment is designed to cover materials from Week 1 to 2. Assignment 1 will be made available on Wattle by 3:00 pm on 4 March. It will involve obtaining suitable life table data, performing exploratory data analysis using R, and writing a report.
Due date: Friday 18th March 3:00pm, Canberra time (Week 4)
Assessment Task 2
Learning Outcomes: 1-2
Mid Semester Exam
The mid-semester exam will be a Wattle-based online exam in approximately Week 6 or Week 7 and it is redeemable. An assessment is redeemable if, when a student performs better in the final exam than in the assessment, then the final exam mark will count instead of that assessment. It will be around 1.5 hours long and will cover materials from Week 1 to 5. Students will be notified regarding examination materials and conditions via Wattle and the examination office.
Due date: Specific date to be advised
Assessment Task 3
Learning Outcomes: 1-2
The students are expected to complete this assignment individually. This assignment is designed to cover materials from Week 1 to 6. Assignment 2 will be made available on Wattle by 3:00 pm on 22 April. It will include data analysis conducted in the R statistical environment and writing a report.
Due date: Friday 6th May 3:00pm, Canberra time (Week 9)
Assessment Task 4
Learning Outcomes: 1-6
The final examination will be a Wattle-based online exam during the university examination period at the end of semester. The final examination will be around 3 hours long and cover the entire syllabus. It will be open book and all materials are permitted. The final examination is worth 55% of the final raw score. The exam will be centrally timetabled and details of the final examination timetable will be made available on the ANU Timetabling website.
Due date: Specific date to be advised
Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.
The ANU commits to assisting all members of our community to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to be familiar with the academic integrity principle and Academic Misconduct Rule, uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with.
The Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.
You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.
There is no hardcopy submission in the course.
No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded
Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.
Assignments will be returned to students online.
Extensions and Penalties
Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.
Resubmission of Assignments
It will not be possible to resubmit assignments.
Distribution of grades policy
Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.
Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.
Support for students
The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).
- ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
- ANU Diversity and inclusion for students with a disability or ongoing or chronic illness
- ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
- ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
- ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
- ANUSA supports and represents undergraduate and ANU College students
- PARSA supports and represents postgraduate and research students
Model selection, robust statistics, high-dimensional data analysis, lasso, PCA, spatio-temporal.
Dr Le Chang