This course will provide students with an overview of the theory and applications of advanced quantitative methods in epidemiology. The purpose of the course is to assist students in answering complex etiological research questions in epidemiology. The course includes three modules: 1) introduction to survival analysis; 2) Cox proportional hazards model and it’s extensions; and 3) multi-state models for event history data.
The course follows a modular format. Each module consists of three lectures and one laboratory. The evaluation for the course will include three assignments (90% of final grade). Each assignment will be based on an existing data set collected from a large cohort study. Students will be provided with a specific question to answer and expected to submit a five page paper presenting their analysis and results. Participation (10% of final grade) will be based on class attendance and active involvement and participation in class discussion.
Contact Brendan Smith (Course Director) for more information about the course.
Module 1. Introduction to Survival Analysis
Review the basic techniques for survival: definitions (dependent variable, origin of time, study window), censoring, important functions describing survival distribution, life tables and Kaplan-Meier estimation.
Convert person data into person-period data, including how to incorporate time-varying covariates.
Review the basic techniques for discrete-time survival models.
Module 2. Cox proportional hazards model and it’s extensions
Review the basic techniques for survival using the Cox proportional hazards model. Understand and analyze the time to event outcome using either parametric models or Cox proportional hazards model.
Assess the assumptions for Cox proportional hazards model and understand the bias when they are not fulfilled.
Understand parametric models, their use and interpretation. Understand the intricacies of power calculation for survival. Calculate power for a 2 arm randomized study.
Be able to identify competing risks and analyze efficiently time to event data in the presence of competing risks. Conduct an analysis for data which has competing risks events.
Module 3. Multi-state models for event history data
Introduce definitions and counting process notation for building multistate models (this includes understanding transition intensity functions, transition intensity matrices, and transition probability matrices).
Understand likelihood construction and parameter estimation for multistate models under complete observation.
Discuss Markov and Semi-Markov multistate model assumptions.
Understand how to structure data for conducting multistate analyses.
Understand how to incorporate covariates into a multistate model.
Understand how multistate models are related to survival models and competing risks models, and be able to conduct a competing risks data analysis using multistate methods.
Know how to perform multistate analyses under the presence of intermittent observation.
1. Research Methods in Epidemiology I (CHL5404H)
2. Quantitative Methods for Biomedical Research (CHL5406H)