Meeting Information
Instructor: Keshav P. Pokhrel, Ph.D.Meeting Times:
Email: kpokhrel(at)umich.edu
Meeting Location: 2048CB
Office: 2087CB
Office Hours:
Tuesday 11:30 PM- 12:30 PM
Wednesday 12:30 PM- 1:45 PM
Thursday 11:30-12:30 PM
and by appointment
Course Description
"Reliability and Survival Analysis" primarily refers to the study of time-to-event data. The phrase "reliability analysis" is primarily used in engineering domain and the phrase "survival analysis" is popularly used in medical and public health domain refering the study of underlying risk factors of different diseases. We will discuss about the power and limitations of proportional hazard model to explain modern research problems: disease risk assessment, treatment evaluation, product liability, and school dropout to name a few. In particular, we will discuss about descriptive methods of survival data, Kaplan-Meiers curves, regression models for survival data, and accelarated failure time models. A software called "R" will be a major computing workhorse for this course.
Course Objectives
This course has four major objectives: 1) Introduce proportional hazard and accelarated failure time models; 2) Develop and strenghthen statistical computing skills in "R" for survival data; 3) Understand and critique applied research papers in the field of reliability and survival analysis; 4) Guide students through the process of writing a research paper, from data cleaning to variable selection and prediction accuracy.
Student Leanrning Outcome:
Here is a sampling of the questions we will adress in this course:
Textbook
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.
Homework
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. Lowest homework grade will be dropped. For better exam results you need to master all the homewor k problems.
Exams
There will be two mid-term exams, and a final project. To answer the exam questions, you need to preseent clear mathematical reasoning of the statistical methods used to solve the subject problems.
Project
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 communicate in way that a reader without formal statistical training can understand the core paert of your work. 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 procedures.
In Class Assignments and Quizzes
In Class Assignments:
You will get worksheets with problems in the class. You can interact with the friends, look at the notes, books and other online resources to solve the problems. One of the in-class assignment is to present a research question in the class. By the end of 3rd week you are required to find an interesting problem from the areas (eg. business, sociology, biology, sports, public health etc.) of your interest and present it to the class. This will help you to prepare for your project and at the same you are highly likely to earn better score in the exams and quizzes. Quizzes:
Quizzes will be closed book and closed notes. In most cases- structures, styles and diffculty levels of quizzes will be similar to those of exams.
Software
We will use a software called "R". R is a programming language for statistical computing and visualizing the data. It can be downloaded for free from http://www.r-project.org. We will use 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%) | Thursday, October 04 | |
| Exam II (20%) | Tuesday, November 20 | |
| Mini Project I(5%) | Due: Tuesday, October 30 | |
| Mini Project II(5%) | Due: Thursday, November 29 | |
| Quiz (10%) | Sept 20, Oct 04, Oct 23, Nov 08, Nov 29, Dec 06 | |
| In-Class Work (5%) | TBD | |
| Homework (10%) | TBD | |
| Final Project(Report 20%, presentation 5%) | Presentation, December 13 (3:00PM-6:00PM) | |
| Final paper due | Tuesday, December 18 by 6:00 PM | |
Grade Distribution
Your final grade will be based on the weighted average of two mid-term exams, five sets of graded homeworks, two mini-projects, and a final project. Lowest homework grade will be dropped.| Letter Grade | E | D- | D | D+ | C- | C | C+ | B- | B | B+ | A- | A | A+ |
| Percentage | 0-59 | 60-62 | 63-66 | 67-69 | 70-72 | 73-76 | 77-79 | 80-82 | 83-86 | 87-89 | 90-92 | 93-96 | 97-100 |
Important Dates
University Attendance Policy:
A student enrolled in a course (lecture, laboratory, recitation, colloquium, seminar, or other university approved format) is expected to attend every scheduled session of the course. The instructor of each course will make known to the students the course attendance policy with respectto student absences. It is the student’s responsibility to be aware of this policy. The instructor makes the final decision to excuse or not to excuse an absence.Presence or participation is also expectedin online courses. Participation in online courses can take various forms; it is the instructor who determines what form of presence or participation is expected. Students enrolled in online courses are responsible for being aware of that policy/expectation. An instructor is entitled to give a failing gradefor excessive absences or for a student who stops participating in class at some point during the semester.
Disability Statement
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.
Academic Integrity
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
Safety
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
Harassment, Sexual Violence, Bias, and Discrimination:
The University of Michigan-Dearborn recognizes that students have a right to study in a safe atmosphere free of sexual violence, harassment, bias and discrimination. Should you wish to report an incident of sexual assault, harassment, bias and discrimination, visit https://umdearborn.edu/offices/enrollment-management-student-life/incident-and-complaint-reporting.
Tentative Academic Calender
| Week | Chapters/Sections | Topics covered | Remarks |
| Week 1 and 2 (Sept 6, 11, 13) | Chapter 1 | Introduction to Survival Data | |
| Week 3 and 4 (Sept 18, 20, 25, 27) | Chapter 2 | Descriptive methods for Survival Data | |
| Week 5 (Oct 2,4) | Review; Exam I | ||
| Week 6,7 (Oct 9, 11, 18) | Chapter 3 | Regression Model for Survival Data | |
| Week 8(Oct 23, 25) | Chapter 3 | Regression Model for Survival Data | |
| Week 9( Oct 30, Nov 1) | Chapter 4 | Proportional Hazards Regression | |
| Week 10 (Nov 06, 08) | Review ; Exam II | ||
| Week 11,12 (Nov 13, 15, 20) | Review ; Exam II | ||
| Week 13 (Nov 27, 29) | Chapter 5 | Model Development; Variable Selection | |
| Week 14 (Dec 4, 6) | Chapter 5 | Model Development; Variable Selection | |
| Week 15 (Dec 11) | Chapter 6 | Assessment of Model Adequacy; Extensions of Proportional Hazard Models |
Homework
| Description | Remarks | |
R-labs
| Description | Remarks | |
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.
Time to-event data a short review of survival analysis.
Lecture Notes A list comprehensive lecture notes and exaples
text book examples Solution of some textbook problems with R-code
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
Data Search Engine Data Search engine by Google