K-means for shared frailty models

• Published in: BMC medical research methodology Vol. 22; no. 1; pp. 11 - 13
• Main Authors: Govindarajulu, Usha, Bedi, Sandeep
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 12.01.2022; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Clustering (Computers); Datasets; Event history analysis; Frail elderly; Frailty; Heterogeneity; Methods; Modified k-means algorithm; Shared frailty; Simulation; Software; Survival analysis; Iran; Heterogeneity; Survival analysis; Shared frailty; Modified k-means algorithm
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-021-01424-5

The purpose of this research was to see how the k-means algorithm can be applied to survival analysis with single events per subject for defining groups, which can then be modeled in a shared frailty model to further allow the capturing the unmeasured confounding not already explained by the covariates in the model. For this purpose we developed our own k-means survival grouping algorithm to handle this approach. We compared a regular shared frailty model with a regular grouping variable and a shared frailty model with a k-means grouping variable in simulations as well as analysis on a real dataset. We found that in both simulations as well as real data showed that our k-means clustering is no different than the typical frailty clustering even under different situations of varied case rates and censoring. It appeared our k-means algorithm could be a trustworthy mechanism of creating groups from data when no grouping term exists for including in a frailty term in a survival model or comparing to an existing grouping variable available in the current data to use in a frailty model.