| CODE | SOR3241 | ||||||||
| TITLE | Survival Analysis | ||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||
| MQF LEVEL | 6 | ||||||||
| ECTS CREDITS | 4 | ||||||||
| DEPARTMENT | Statistics and Operations Research | ||||||||
| DESCRIPTION | - Probabilities of Death and Survival; - The Force of Mortality; - Initial Rates and Central Rates of Mortality; - Complete Expectation of Life; - Curtate Expectation of Life; - Gompertz and Makeham's Laws of Mortality; - Estimating the Lifetime Distribution; - Censoring Mechanisms; - The Kaplan Meier Estimator; - Comparing Lifetime Distributions; - The Nelson-Aalen Estimator; - The Integrated Hazard Function; - Parametric Models for the Hazard Function; - The Cox Regression Model; - Comparison of Multiple-state, Binomial and Poisson Models; - Exposure to Risk. Study-Unit Aims: - Familiarize with different survival distributions and hazard functions; - Familiarize with different censoring techniques; - Use partial log-likelihood to the estimate parameters; - Discriminate between non-parametric, semiparametric and parametric techniques. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Familiarise with the facilities of STATA and SPSS to fit survival models; - Use appropriate tests to compare between several survival distributions; - Identity the appropriate hazard function in parametric survival applications. 2. Skills: By the end of the study-unit the student will be able to: - Produce survival probabilities using the Kaplan Meier and Nelson Aalen estimators; - Compute 95% confidence intervals for the survival probabilities of these two estimators; - Fit Cox regression models to suffer data when the proportional hazard assumption is satisfied; - Fit parametric survival models when the hazard function is known; - Familiarize with the Binomial and Poisson models of mortality. Statistical packages (SPSS, STATA, MATLAB) will be used throughout the course. Main Text/s and any supplementary readings: Suggested Texts: - Lee, E.T. (1992), Statistical Methods for Survival Data Analysis, John Wiley & Sons. Inc. - Kalbfleisch, J.D. and Prentice, R.L. (2002), The Statistical Analysis of Failure Time Data, John Wiley & Sons. Inc. - Fleming, T.R. and Herrington, D.P. (1991), Counting Processes and Survival Analysis, John Wiley & Sons. Inc. - Miller, R.G. (1981), Survival Analysis, Wiley Classics Library |
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| ADDITIONAL NOTES | Pre-Requisite Study-Units: SOR1110, SOR2211 and SOR3221 | ||||||||
| STUDY-UNIT TYPE | Lecture and Practical | ||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Liberato Camilleri |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
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