• Class Number 4246
• Term Code 3230
• Class Info
• Unit Value 6 units
• Mode of Delivery In Person
• COURSE CONVENER
• Dr Le Chang
• LECTURER
• 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
SELT Survey Results

Survival Models (STAT7042)

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.

## Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1. Communicate the concept of survival models;
2. Describe in detail the estimation procedures for lifetime distributions;
3. Implement complex statistical models of transfer between multiple states, including processes with single or multiple decrements, and derive relationships between probabilities of transfer and transition intensities;
4. Derive maximum likelihood estimators for the transition intensities in complex models of transfers between states with piecewise constant transition intensities;
5. Comprehensively describe how to estimate transition intensities depending on age, exactly or using the census approximation; and,
6. Communicate in detail how to test crude estimates for consistency with a standard table or a set of graduated estimates, and describe the process of graduation.

## Research-Led Teaching

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.

## Required Resources

Lecture notes and other course materials will be updated weekly with the progress of this course on Wattle.

Whether you are on campus or studying remotely, there are a variety of online platforms you will use to participate in your study program. These could include videos for lectures and other instruction, two-way video conferencing for interactive learning, email and other messaging tools for communication, interactive web apps for formative and collaborative activities, print and/or photo/scan for handwritten work and drawings, and home-based assessment.

ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.

## Staff Feedback

Students will be given feedback in the following forms in this course:

• Tutorial solutions and discussions (online and local tutorials)
• lecturer consultations (online)

## Student Feedback

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). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

## Other Information

Scaling

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.

Co- Teaching

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.

## Class Schedule

Week/Session Summary of Activities Assessment
1 Data Types, Introduction to Survival Models
2 Introduction to Survival Models
3 Estimation Methods
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

## Tutorial Registration

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 Summary

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

## Policies

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 Integrity Rule before the commencement of their course. Other key policies and guidelines include:

## Assessment Requirements

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 Skills website. 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.

## Participation

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.

## Examination(s)

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.

Value: 10 %
Due Date: 18/03/2022
Return of Assessment: 01/04/2022
Learning Outcomes: 1-2

Assignment 1

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.

Value: 10%

Due date: Friday 18th March 3:00pm, Canberra time (Week 4)

Value: 25 %
Due Date: 28/03/2022
Return of Assessment: 06/05/2022
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. Exact details (e.g. exact length) of the exam will be notified to all students via Wattle no later than Week 4 of the semester. The exam will be centrally timetabled and details of the mid-semester exam timetable will be made available on the ANU Timetabling website.

Value: 25%

Due date: Specific date to be advised

Value: 10 %
Due Date: 06/05/2022
Return of Assessment: 20/05/2022
Learning Outcomes: 1-2

Assignment 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.

Value: 10%

Due date: Friday 6th May 3:00pm, Canberra time (Week 9)

Value: 55 %
Due Date: 02/06/2022
Return of Assessment: 30/06/2022
Learning Outcomes: 1-6

Final Exam

The students are to complete this assessment individually. 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. Exact details (e.g. exact length) of the exam will be notified to all students via Wattle no later than Week 10 of the semester. The exam will be centrally timetabled and details of the final examination timetable will be made available on the ANU Timetabling website.

Value: 55%

Due date: Specific date to be advised

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.

The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

## Online Submission

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.

## Hardcopy Submission

There is no hardcopy submission in the course.

## Late Submission

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

## Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

## Returning Assignments

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.

## Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

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).

## Convener

 Dr Le Chang 6125 5116 le.chang@anu.edu.au

### Research Interests

Model selection, robust statistics, high-dimensional data analysis, lasso, PCA, spatio-temporal.

### Dr Le Chang

 Wednesday 09:30 11:00 Wednesday 09:30 11:00

## Instructor

 Dr Le Chang 6125 5116 le.chang@anu.edu.au

### Dr Le Chang

 Wednesday 09:30 11:00 Wednesday 09:30 11:00