STAT -545-Reliability and Survival Analysis- Fall 2016
Applied Survival Analysis: Regression Modeling of Time to Event Data , 2nd Edition by Hosmer, Lemeshow and May
Instructor: Keshav P. Pokhrel, Ph.D.
Meeting Times: MW 3:30 PM - 4:45PM
Meeting Location: 2046CB
Monday 2:00PM - 3:15 PM
Tuesday 2:00PM - 3:00 PM
Wednesday 2:00PM - 3:15 PM
and by appointments
Course Description and Objectives
The primary focus of this course is to shed light on the analysis of time-to-event data. Survival methods are
considered to be reasonable to incorporate the variations from both uncenceroed and cencored observations. Students are expected to learn some parametric, nonparametric and semiparametric approches to analyse survival data. In particular, we will discuss about descriptive methods, Kaplan-Meiers curves, regression models for survival data, and proportional hazard models. The major workhorse for this course is a Statistical software "R".
One of the major objective of this course to model and test differences in survival times of two or more groups of interest together with the effect of one or more variable on survival time. We will start from cleaning time to event data and end with some useful and inetpretable survival models. In a nutshell, this coursre intends to develop and nurture students interest to explore more about time-to-event data. This course is meant to be a window to peek the wonderful paradiegm of survival analysis.
Student Leanrning Outcome:
At the end of the course, the student will be able to
understand to model censored and nocensored data
develop statistical models for the analysis of time-to-event data
Applied Survival Analysis: Regression Modeling of Time to Event Data, 2nd Edition by David W. Hosmer, Jr., Stanley Lemeshow, Susanne May.
Major Reference Books
- Survival Analysis: Techniques for Censored and Truncated Data by John P. Klein and Melvin L. Moeschberger, ISBN 038794829 5, Springer.
- The Statistical Analysis of Failure Time Data (2nd Edition) by John D. Kalbfleisch and Ross L. Prentice (Wiley Series in Probability and Statistics, 2002)
- Diez, Barr, and Cetinkaya-Rundel, OpenIntro Statistics.
At least five sets of homework problems will be assigned. Some addition homework problems will periodically be assigned during the lecture. Some addition homework problems, especially class presentations, will periodically be assigned during the lecture. Good news! Lowest homework grade will be dropped. For better exam results you need to master all the homework problems.
There will be two mid-term exams, and a final project. To answer the exam questions, you are expected to have a clear mathematical reasoning of the statistical methods used to solve the subject problems.
There will be two mini-projects and a semester project. For a good project, you need to describe the data, pose reasonable hypotheses, select appropriate time series model/s, compute the test results, and explain the results in both statistical terms and in plain English. Primary objective of these projects is to apply statistical methods in the real life situations and come up with logical reasonings and explanations of the statistical methods.
We use a software called "R". R is a programming language for statistical computing and visualizing data. It can be downloaded for free from http://www.r-project.org. We will R Studio for regular classroom activities. R studio is an open source Integrated development Environment(IDE) for R.
To download R click here for windows and here for Mac.
After Installing R: click R Studio to download R studio.
| Evaluations and Important Dates:|
|Exam I (20%) || Wednesday, October 05|
|Exam II (20%)|| Wednesday, November 09|
| Mini Project I(10%) || Due, October 26 |
| Mini Project II(10%) || Due, November 30 |
| 5 Homeworks (15%) || TBD |
| Final Project(25%) || Presentation, December 12 |
| Final paper due|| December 19 |
Your final grade will be based on two mid-term exams, five sets of graded homeworks, two mini-projects, and a final project. Lowest homework grade will be dropped.
The University will make reasonable accommodations for persons with documented disabilities. Student need to register with Disability Resource Services (DSR) every semester they are enrolled for classes. DRS is located in counseling & Support Services, 2157 UC. To be assured of having services when they are needed, students should register no later than the end of add/ drop deadline of each term. Visit the DSR website at: webapps.umd.umich.edu/aim. If you have disability that necessitates an accommodation or adjustment to the academic requirements stated in this syllabus, you must register with DRS as directed above and notify me. Upon receipt of your notification, we will make accommodation as directed by DRS.
￼The University of Michigan-Dearborn values academic honesty and integrity. Each student has a responsibility to understand, accept, and comply with the University's standards of academic conduct as set forth by the Code of Academic Conduct ￼(mdearborn.edu/policies_st-rights), as well as policies established by each college. Cheating , collusion, misconduct, fabrication, and plagiarism are considered serious offenses, and may be monitored using tools including but not limited to TurnItIn. Violations can result in penalties up to and including expulsion from the University. At the instructor's direction, the penalty may be a grade zero on the assignment up to and including recommending that student be expelled from the University. It is the sole responsibility of the student to understand and follow academic guidelines regarding plagiarism. The University of Michigan-Dearborm has an online academic integrity tutorial that can be accessed at: umdearborn.edu/umemergencyalert
All students are strongly encouraged to register in the campus Emergency Alert System, used to communicate with campus community during emergency. More information on the system and how it works, along with enrollment information can be found at: webapps.umd.umich.edu/aim
Tentative Academic Calender
| Week ||Chapters/Sections||Topics covered ||Remarks |
| Week 1 and 2 (Sept 7, 12, 14) || Chapter 1 || Introduction to Survival Data|
| Week 3 and 4 (Sept 19, 21, 26, 28) || Chapter 2 || Descriptive methods for Survival Data || |
| Week 5 (Oct 3,5) || || Review; Exam I || |
| Week 6,7 and 8 (Oct 10, 12, 19, 24) || Chapter 3 || Regression Model for Survival Data || |
| Week 9, 10, and 11 ( Oct 26, 31, and Nov 2) || Chapter 4 || Proportional Hazards Regression || |
| Week 12 (Nov 7, 9) || || Review ; Exam II || |
| Week 13, 14 (Nov 14, 16, 21, 23) || Chapter 5 || Model Development; Variable Selection || |
| Week 15, 16 (Nov 28, 30, Dec 5, 7) || Chapter 6 || Assessment of Model Adequacy; Extensions of Proportional Hazard Models || |
Some Helpful Resources
UMASS data library A comprehensive source of data used in our text book.
Try R A good resourse to learn R online
R tutorials Yet another collection of resourses to learn R
Exploratory Data Analysis Wide range of statistical topics are covered in this web page with video lectures and other supplementary materials.
More Stat Apps Wonderful collection of Statistics Apps for data visualization
Machine Learning repository UCI Machine Learning Repository- a comprehensive webpage with varities of data sets.
List of data A rich collection of data
Data Journalism Open data sets by British newspaper "theguardian".
StatSci.org. A good resource for varieties of data sets. These data sets are open to public and you can use these data sets for your own projects. If you happen to use these data please do not forget to mention the source.
OpenItro This is an excellent resource for introductory statistics. Apart from lecture notes they also have well explained examples with R code.
Markdown Themes Appearance and Style themes to create HTML document using R Studio.
Shiny Apps A comprehensive Resource of Shiny Apps