Survival Analysis: Part II - Methods reducing a gap between statistics and real world.

Survival Analysis: Part II - Methods reducing a gap between statistics and real world. Korean J Anesthesiol. 2019 May 17;: Authors: In J, Lee DK Abstract Following the previous article, this review provides several in-depth concepts of survival analysis. Also, several code for the specific survival analysis are also listed to enhance the understanding about survival analysis and to provide the applicable method for survival analysis. Proportional hazard assumption is one of important concepts in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, graphical analysis method and the goodness of fit test are introduced with detailed code and examples. In case of the violated proportional hazard assumption, the extended models of Cox regression are required. The simplified concepts of stratified Cox proportional hazard model and time-dependent Cox regression are also described. Source code for actual analysis in available statistical package with detailed results interpretation could enable to realize survival analysis with personal data. To enhance statistical power of survival analysis, evaluation about basic assumptions and interaction between variables and time is important. By doing this, survival analysis could provide a reliable scientific result with confidence. PMID: 31096731 [PubMed - as supplied by publisher]
Source: Korean Journal of Anesthesiology - Category: Anesthesiology Tags: Korean J Anesthesiol Source Type: research