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Workshop Title Slide

Survival Analysis with R

Many research questions in health, clinical, and epidemiological research focus not only on whether an event occurs, but also on when it occurs. Survival analysis provides a powerful set of statistical techniques for examining time-to-event outcomes such as mortality, disease recurrence, hospitalization, or recovery. Through live demonstrations and hands-on exercises in R, participants will gain practical experience analyzing and interpreting survival data using widely used survival analysis methods. This workshop introduces the fundamentals of survival analysis, including Kaplan-Meier survival curves, log-rank tests, and Cox proportional hazards regression. Participants will learn how to prepare time-to-event data, compare survival experiences across groups, and model factors associated with survival outcomes. Whether you are conducting clinical, epidemiological, or health services research, this session will provide a practical foundation for applying survival analysis techniques in R.

By the end of this workshop, participants will be able to:

  • Prepare and manage time-to-event data for survival analysis in R.
  • Estimate and interpret Kaplan-Meier survival curves.
  • Compare survival distributions between groups using the log-rank test.
  • Fit and interpret Cox proportional hazards regression models.
  • Assess the proportional hazards assumption and perform model diagnostics.
  • Create publication-ready tables, figures, and summaries to communicate survival analysis results effectively.

This session is ideal for researchers, graduate students, and analysts working with time-to-event data. Basic familiarity with R and introductory statistical concepts is recommended.

Workshop Preparation

None

Facilitator Bio

Shubrandu (he/him) is a PhD Candidate in Health Research Methodology (Clinical Epidemiology) at McMaster University with a background in epidemiology and biostatistics. He has extensive experience working with large healthcare datasets and supports researchers in study design, statistical analysis, and data management. His expertise includes regression modeling, survival analysis, and handling administrative health data, with proficiency in R and SAS.

Workshop Slides

Coming soon.