Course Overview
(AST305) Lifetime Data Analysis I
Course Teacher
Md Rasel Biswas
MS & BS in Applied Statistics (DU)
Faculty member at DU since July 2022
Email: rasel@du.ac.bd
Personal Website: https://rasel.rbind.io
Course introduction
Course Title: Lifetime Data Analysis I
Course Code: AST305
Credit Hour: 3
This course deals with the analysis of time-to-event data (also known as survival or failure time data), which are commonly encountered in scientific investigations. It is being extensively used in medicine, clinical trials, biological and epidemiological studies, engineering, economics, and social sciences. This course provides an opportunity for students to learn lifetime probability distributions that are useful for modeling tine-to-event data. Topics include lifetime distributions and non-parametric and parametric approaches for analyzing time-to-event data.
Course objectives
Upon completing this course, students will be able to:
Identify and analyze censored data using appropriate statistical methods and models.
Understand and apply the theory and methodology for analyzing lifetime data from both complete and censored samples.
Interpret statistical lifetime distributions, types of censoring, and utilize graphical techniques for data exploration.
Implement non-parametric and parametric estimation methods in survival analysis.
Lecture plans
Lecture 1- 6: Basic concepts and models: lifetime distributions-continuous models, discrete models, a general formulation; some important models-exponential, Weibull, log-normal, log-logistic, gamma distributions, log-location-scale models, inverse Gaussian distributions models, mixture; regression models.
Lecture 7- 10: Observation schemes, censoring, and likelihood: right censoring and maximum likelihood; other forms of incomplete data; truncation and selection effects; information and design issues.
Lecture 11- 17: Nonparametric and graphical procedures: nonparametric estimation of survivor function and quantiles; descriptive and diagnostic plots; estimation of hazard or density functions; methods of truncated and interval censored data; life tables.
Lecture 18- 24: Inference procedures for parametric models: inference procedures for exponential distributions; gamma distributions; inverse Gaussian distributions; grouped, interval censored, or truncated data; mixture models; threshold parameters; prediction intervals.
Lecture 25- 30: Inference procedure for log-location-scale distributions: inference for location-scale distributions; Weibull and extreme-value distributions; log-normal and log-logistic distributions; comparison of distributions; models with additional shape parameters; planning experiment for life tests.
Textbooks
Lecture time
Sundays, 10:00–11:20 AM; Tuesdays, 11:20 AM–12:50 PM.
Assessment
- Attendance: 5%
- Incourse exams: 25%
- Final exam: 70%