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Survival Analysis

East Baltimore
1st term
3 credits
Academic Year:
2022 - 2023
Instruction Method:
Class Times:
  • Tu Th,  3:30 - 4:50pm
Auditors Allowed:
Yes, with instructor consent
Undergrads Allowed:
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Yuxin Zhu

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


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:

Special Comments:

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