Stanford Root

Schedule

Stanford Root

Schedule

STATS 331

Survival Analysis (BMDS 252)

UNITS:3
GRADING:Medical Option (Med-Ltr-CR/NC)
LEVEL:Graduate
GER:—

The course introduces basic concepts, theoretical basis and statistical methods associated with survival data. Topics include censoring, parametric model for survival time, Kaplan-Meier estimation, logrank test, proportional hazards regression, restricted mean survival time, survival analysis in group-sequential clinical trial, and extensions such as competing risk, multivariate survival time and recurrent event data. The traditional counting process/martingale methods as well as modern empirical process methods will be covered. The course will focus on providing methodological basis of survival analysis. Prerequisite: Understanding of basic probability theory and statistical inference methods.

Syllabus for selected term:
View Winter 2027 Syllabus

Sections

1 Term
Lecture 1Open
ID: 13289
0 / 30 enrolled
DAYS:Tuesday, Thursday
TIME:11:30 AM – 12:50 PM
LOCATION:TBD
INSTRUCTOR:
Shih, Mei-Chiung, Tian, Lu, Bi, Dehua, Lu, Ying
3units

STATS 331: Survival Analysis (BMDS 252)

3 units · Medical Option (Med-Ltr-CR/NC)

The course introduces basic concepts, theoretical basis and statistical methods associated with survival data. Topics include censoring, parametric model for survival time, Kaplan-Meier estimation, logrank test, proportional hazards regression, restricted mean survival time, survival analysis in group-sequential clinical trial, and extensions such as competing risk, multivariate survival time and recurrent event data. The traditional counting process/martingale methods as well as modern empirical process methods will be covered. The course will focus on providing methodological basis of survival analysis. Prerequisite: Understanding of basic probability theory and statistical inference methods.

Offered in Winter 2027 at Stanford University.

Winter 2027 sections

  • Lecture — Tuesday Thursday 11:30 AM – 12:50 PM — Shih, Mei-Chiung, Tian, Lu, Bi, Dehua, Lu, Ying (Graduate)

More STATS courses

  • STATS 311: Information Theory and Statistics (EE 377)
  • STATS 315A: Modern Applied Statistics: Learning
  • STATS 318: Modern Markov Chains (MATH 235)
  • STATS 319: Literature of Statistics
  • STATS 322: Function Estimation in White Noise
  • STATS 323: Sequential Analysis (STATS 223)
  • STATS 354: Generalization and Causality in Biohealth (CS 273D)
  • STATS 357: Reliability and Validity in Artificial Intelligence (MS&E 330)
  • STATS 361: Causal Inference
  • STATS 362: Topic: Monte Carlo
  • STATS 363: Design of Experiments (STATS 263)
  • STATS 369: Methods from Statistical Physics

All STATS courses · All departments