140.641.01
Survival Analysis

Prerequisite:

Biostatistics 140.621-4 or 140.651 or equivalent. Calculus I and II. Knowledge of fundamental probability and statistical theory is required.

Description:

Introduces fundamental concepts, theory and methods in survival analysis. Emphasizes statistical tools and model interpretations which are useful in medical follow-up studies and in general time-to-event studies. Includes hazard function, survival function, different types of censoring, Kaplan-Meier estimate, log-rank test and its generalization. For parametric inference, includes likelihood estimation and the exponential, Weibull, log-logistic and other relevant distributions. Discusses in detail statistical methods and theory for the proportional hazard models (Cox model), with extensions to time-dependent covariates. Includes clinical and epidemiological examples (through class presentations). Introduces basic concepts and methods for competing risks data, including the cause-specific hazard models and other models based of cumulative incidence function (CIF). Illustrates various statistical procedures (through homework assignments).

Learning Objectives:

Upon successfully completing this course, students will be able to:

1. Understand features of time-to-event data
2. Explain fundamental concepts in survival analysis
3. Describe statistical methods which are useful in medical follow-up studies and in general time-to-event studies
4. Properly use software and packages to conduct time-to-event data analysis

Methods of Assessment:

This course is evaluated as follows:

• 60% Homework
• 40% Final Exam

Instructor Consent:

Consent required for some students

Consent Note:

Consent required for non-Biostatistics students

For consent, contact:

yzhu15@jhu.edu

Special Comments:

Students must attend 2 one-hour lab sessions per week.