K-means for shared frailty models

Background 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...

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Vydáno v:BMC medical research methodology Ročník 22; číslo 1; s. 11 - 13
Hlavní autoři: Govindarajulu, Usha, Bedi, Sandeep
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 12.01.2022
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Algorithms
Analysis
Clustering (Computers)
Datasets
Event history analysis
Frail elderly
Frailty
Health Sciences
Heterogeneity
Medicine
Medicine & Public Health
Methods
Modified k-means algorithm
Shared frailty
Simulation
Software
Statistical Theory and Methods
Statistics for Life Sciences
Survival analysis
Theory of Medicine/Bioethics
Iran
Survival analysis
Heterogeneity
Shared frailty
Modified k-means algorithm
ISSN:1471-2288, 1471-2288
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Shrnutí:Background 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. Methods 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. Results 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.
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ISSN:1471-2288
1471-2288
DOI:10.1186/s12874-021-01424-5