SDST3655 Survival analysis (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota ---
Course Co-ordinator Prof Y Gu, SCDS (Department of Statistics and Actuarial Science) < yugu@hku.hk >
Teachers Involved (Prof Y Gu,Statistics & Actuarial Science)
Course Objectives This course is concerned with how models which predict the survival pattern of humans or other entities are established.  This exercise is sometimes referred to as survival-model construction.
Course Contents & Topics The nature and properties of parametric and nonparametric survival models will be studied.  Topics to be covered include: the introduction of some important basic quantities like the hazard function and survival function; some commonly used parametric survival models; concepts of censoring and/or truncation; parametric estimation of the survival distribution by maximum likelihood estimation method; nonparametric estimation of the survival functions from possibly censored samples by means of the Kaplan-Meier estimator, the Nelson-Aalen estimator; and the kernel density estimator or the Ramlau-Hansen estimator and comparisons of k independent survival functions by means of the generalized log-rank test; parametric regression models; Cox's semiparametric proportional hazards regression model; and multivariate survival analysis.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 acquire a clear understanding of the nature of failure time data or survival data, a generalization of the concept of death and life
CLO 2 perform estimation for some commonly used survival models under different types of censoring mechanisms
CLO 3 analyze survival data using the Cox's semiparametric proportional hazards model
CLO 4 extend the Cox's model to a multivariate setup to accommodate multivariate survival data
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST3600 or SDST3907, or already enrolled in this course.
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2024 Major in Decision Analytics ( Disciplinary Elective )
2024 Major in Risk Management ( Disciplinary Elective )
2024 Major in Statistics ( Disciplinary Elective )
2024 Minor in Statistics ( Disciplinary Elective )
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2023 Major in Decision Analytics ( Disciplinary Elective )
2023 Major in Risk Management ( Disciplinary Elective )
2023 Major in Statistics ( Disciplinary Elective )
2023 Minor in Statistics ( Disciplinary Elective )
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2022 Major in Decision Analytics ( Disciplinary Elective )
2022 Major in Risk Management ( Disciplinary Elective )
2022 Major in Statistics ( Disciplinary Elective )
2022 Minor in Statistics ( Disciplinary Elective )
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2021 Major in Decision Analytics ( Disciplinary Elective )
2021 Major in Risk Management ( Disciplinary Elective )
2021 Major in Statistics ( Disciplinary Elective )
2021 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping 2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2024 Major in Decision Analytics < PLO 1,2,3 >
2024 Major in Risk Management < PLO 3,4 >
2024 Major in Statistics < PLO 1,2,4,5,6 >
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2023 Major in Decision Analytics < PLO 1,2,3 >
2023 Major in Risk Management < PLO 3,4 >
2023 Major in Statistics < PLO 1,2,4,5,6 >
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2022 Major in Decision Analytics < PLO 1,2,3 >
2022 Major in Risk Management < PLO 3,4 >
2022 Major in Statistics < PLO 1,2,4,5,6 >
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2021 Major in Decision Analytics < PLO 1,2,3 >
2021 Major in Risk Management < PLO 3,4 >
2021 Major in Statistics < PLO 1,2,4,5,6 >
Offer in 2025 - 2026 Y        2nd sem    Examination May     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, tutorials, and a class test) 40.0 1,2,3,4
Examination One 2-hour written examination 60.0 1,2,3,4
Required/recommended reading
and online materials		 Cox, D. R. and Oakes, D.: Analysis of Survival Data (Chapman and Hall, 1984)
Hosmer, D. W. and Lemeshow, S.: Applied Survival Analysis: Regression Modeling of Time to Event Data (Wiley, 1999)
Klein, J. P. and Moeschberger, M. L.: Survival Analysis: Techniques for Censored and Truncated Data (Springer Verlag, New York, 2005, 2nd ed.)
Course Website http://moodle.hku.hk
Additional Course Information NIL