STA 312: Survival Analysis
University of Toronto Mississauga, Fall 2023
http://utstat.toronto.edu/brunner/312f23
Lecture: Lecture Tuesday 4:10 - 6:00 p.m. in DV2074 and Thursday 4:10 - 5:00 p.m. in DH2060
Note: Jerry does not read his email every day. It is much more efficient to talk with him before or after class, or during office hours.
Tutorial: Friday 5:10 - 6:00 p.m. in DV2074 -- except that on October 20 and November 17, the tutorial will be in DH2060.
Text: Applied survival analysis using R by Dirk F. Moore. It is a free download from the U of T library. Additional reading material is available on the course home page.
Learning Objectives: The objective of the course is for students to understand the theory of survival analysis at an intermediate level, and to be able to analyze survival data. Understanding of the theory will be demonstrated by deriving important results and solving homework problems that apply the concepts. Ability to analyze data will be demonstrated by running R on sample data sets, and writing reasonable interpretations of the results.
Topics: Math stat review, Maximum likelihood, Censoring, Survival function, Distributions, Hazard and cumulative hazard function, Parametric analysis for a single sample, Life tables, Kapan-Meier estimates, Parametric regression for censored data, Proportional hazards model, Time-varying covariates.
Prerequisites: STA260 or STA261. STA302 is recommended.
Grading:
In spite of the mark weighting scheme above, a good performance on the final exam can save a student from failing the course. Suppose your final average including the final exam is less than 50%. If your mark on the final exam is at least 70%, or your mark on the final is at or above the class median, then you get a mark of 50% for the course. This rule is intended to give hope to students who have messed up on the quizzes, and encourage them to study for the final exam.
There will be an assignment for each quiz. The knowledge you need to do each quiz is a subset of the knowledge you need to do the corresponding assignment. Some (most) of the assignments include a computer part. You will bring printouts to the quiz and answer questions based on the printouts. Possibly, one of the quiz questions will be to hand in a printout. The non-computer parts of the assignments are just to prepare you for the quizzes; they will never be handed in.
Policy for missed work: If you miss a quiz, the mark is zero. However, your lowest quiz mark will be dropped. If you miss a regular quiz or a pop quiz with a valid excuse, the other quizzes will count for more. The lowest mark will still be dropped.
What is a valid excuse? Medical issues and family emergencies are valid. Vacations are not. Automotive breakdown or other transportation problems are never valid excuses. If you miss term work because you are taking another class at the same time as this one, that is not a valid excuse. The printer jammed, my dog ate it, etc. fall into the same category. If the University is officially open, weather is a valid excuse only if more than 50% of the class miss the quiz.
Academic Honesty: It is an academic offence to present someone else's work as your own, or to allow your work to be copied for this purpose. To repeat: the person who allows her/his work to be copied is equally guilty, and subject to disciplinary action by the university.
The main rule is don't copy anyone's quiz or computer work, and don't let anyone else copy from you. You are expected to do the work yourself. For the computer work, it's okay to compare numerical answers. A good rule is to never help someone who hasn't started yet.
If you are asked to hand in hard copy of your computer work as part of a quiz, your name and student number should be on the printout. You are allowed to write your name and student number on the printout in advance, but do not write anything else on your printouts in advance.
This should be obvious, but if you are asked for a number from your printout and you don't have a printout, do not answer the question and pretend you remembered the number. If you do, you will be charged with an academic offence. This also should be obvious, but you are not allowed to put answers or any other material related to the non-computer questions in comment statements, or otherwise cause such material to appear on your printout.
Generative AI: In this class, the use of artificial intelligence tools like chatGPT is not particularly recommended, but it is not forbidden either. In particular, if a homework problem requires you to do something in R and you are asked to bring hard copy of your input and output to the quiz, it is okay if part or all of the R code was generated by AI. It might be completely right, it might be partly right, or it might be completely wrong. There may be syntax errors. You are responsible for the result.
It is still an academic offence to present someone else's work as your own, or to allow your work to be copied by another student. So, if a classmate does a computer assignment using AI and then gives you the result, you are both guilty of an academic offence.
AI can do a surprisingly good job on some of the math stat homework problems. It can also produce answers that have serious flaws or are off topic. Again, you are responsible for what you write. Some time during the term, I expect to hear "But this is what chatGPT said!" That's never a valid argument. Your job is to understand.
Accessibility Needs: We are committed to accessibility. If you require accommodations for a disability, or have any accessibility concerns about the course, the classroom or course materials, please contact Jerry or Accessibility Services (visit http://www.utm.utoronto.ca/accessability or email accessconfirm.utm@utoronto.ca) as soon as possible.