Survival Predictive Analysis and Meta-Analysis Practice
Survival analysis is one of the main tools for assessing evidence in clinical research. Frequently, multiple estimates of effects from multiple survival studies are used to create a stronger evidence base by conducting a meta-analysis. Survival data has specific metrics that need to be complementary to each other to be combined in a meta-analysis. Also, the context surrounding the data needs to be evaluated carefully before deciding whether the data is appropriate for meta-analysis. The code for implementing the meta-analysis in Python is very specific, and we will be discussing it in this chapter.
In this chapter, we’re going to cover the following main topics:
- Understanding survival and meta-analysis data
- Implementing the DerSimonian and Laird inverse variance method and investigating heterogeneity in meta-analysis
- Plotting the forest plots for meta-analysis
- Mastering meta-regression
