Summary
In this chapter, we learned about the basics of survival analysis, including where it is used and how we can improve drug evaluation. We also learned about its use in biomedical research through the use of time series data. Also, we learned how to implement survival analysis using two Python packages, scikit-survival and lifelines. We evaluated different exemplar data in the oncology area, the veteran lung cancer and breast cancer datasets, from the scikit-survival package. Finally, we covered the theory and practice of Kaplan-Meier plots, logrank tests, and Cox regression typically used in survival analysis. We learned how to compare different treatments and survival rates of different groups using Kaplan-Meier curves. Finally, we learned how to implement these methods and interpret the results using real-world survival data.
In the next chapter, we will learn about meta-analysis. We will also learn how to synthesize the evidence from multiple studies.
