Module date/time: Friday, July 27, 8:30 a.m.-5 p.m.
This module will cover advanced topics in survival analysis, with an emphasis on applications in studies relying on observational data, including epidemiologic cohort studies and comparative effectiveness studies. This module will:
- Describe Cox regression models suitable for examining adjusted associations and effect modification;
- Cover the important choice of the time scale for the analysis, and discuss how to analyze data on subjects who enter observation after time zero (left entry and left truncation);
- Cover methods for appropriate inferences when there are competing risks, including the Cox regression model for cause-specific hazard functions and the Fine-Gray model for hazards related to the cumulative incidence function;
- Discuss biases that can arise in observational data: confounding, immortal time bias and index event bias, and how to treat them in the analysis, and
- Cover issues related to time-dependence in the Cox regression model, including how to incorporate hazard ratios, exposures, and adjustment variables that depend on time using time-dependent coefficients, time dependent covariates, and time-dependent stratification.
The course will focus on application and understanding the concepts with examples from the biomedical literature; mathematical details will be kept to a minimum. Knowledge of material in the Module 11: Introduction to Survival Analysis will be assumed.
Access 2017 Course Materials (2018 materials will be uploaded to this page prior to the start of the module)