Survival Analysis in Biomedical Research
Many branches of clinical biostatistics rely on survival analysis. It is the main biostatistical tool in most clinical trials and is used to evaluate drug efficacy and effectiveness in different experimental and observational settings.
In this chapter, we’re going to cover the following main topics:
- Understanding survival analysis and how it is used in biomedical research
- Creating Kaplan-Meier curves in Python
- Implementing Cox (proportional hazards) regression in Python
By the end of this chapter, you will gain a deeper understanding of how these methods are applied in biomedical research to analyze and interpret clinical trial data for survival analysis.
