Spring 2022 - STAT 855 G100
Lifetime Data Analysis (4)
Class Number: 6918
Delivery Method: In Person
Statistical methodology used in analysing failure time data. Likelihoods under various censoring patterns. Inference using parametric regression models including the exponential, Weibull, lognormal, generalized gamma distributions. Goodness-of-fit tests. The proportional hazards family, and inference under the proportional hazards model. Stratification and blocking in proportional hazards models. Time dependent covariates. Regression methods for grouped data. Students with credit for STAT 806 may not take this course for further credit.
This course introduces to students the most important statistical approaches in analyzing event history data. It includes parametric inferences with likelihood functions under various censoring patterns, and the essential topics in non/semiparametric inferences such as the Kaplan-Meier estimator, the logrank test, and the Cox proportional hazards model. Some advanced topics will be covered, including counting process framework, various forms of incomplete lifetime data (e.g., competing risks, interval censoring, and truncation), recurrent events and multi-state process, and alternative regression models to the Cox proportional hazards model. Below is a tentative outline of the course.
2. Likelihood-Based Parametric Inferences
3. Kaplan-Meier Estimator, Logrank Test, Cox Proportional Hazards Model
4. Counting Process Framework
5. Competing Risks, Interval Censoring and Truncation
6. Recurrent Events and Multi-State Process
7. Alternative Regression Models
- Assignments 40%
- Projects 60%
Above grading is subject to change
Survival Analysis: Techniques for Censored and Truncated Data, 2nd ed., by John Klein and Melvin Moeschberger. Publisher: Springer
1. Analysis of Survival Data, by Cox and Oakes
2. Counting Processes and Survival Analysis, by Fleming and Harrington
3. The Statistical Analysis of Failure Time Data, by Kalbfleisch and Prentice
4. The Statistical Analysis of Recurrent Events, by Cook and Lawless
5. Statistical Models and Methods for Lifetime Data, by Lawless
6. Statistical Models Based on Counting Processes, by Andersen, Borgan, Gill and Keiding
7. Survival Analysis Using SAS: A Practical Guide, Author: Paul Allison, Publisher: SAS Publishing
Graduate Studies Notes:
Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.
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TEACHING AT SFU IN SPRING 2022
Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place. Some courses will still be offered through remote methods, and if so, this will be clearly identified in the schedule of classes. You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).
Enrolling in a course acknowledges that you are able to attend in whatever format is required. You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.
Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (firstname.lastname@example.org or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.