Model Performance Evaluation in Clustered Survival Data: A Simulation Study

Abstract

Objective: Clustered data structure is a frequently encountered data type today. Like the other analysis when performing a survival analysis, clustered data type should be taken into account. The aim of this study is to compare some survival analysis used for clustered data and Cox regression analysis. Material and Method: The study consists of three parts. In the scenarios in the first section, cluster size and number of individuals change in balanced cluster sizes. In the second part, the effect of changes in the number of individuals in unbalanced cluster sizes, and in the third section, only the effect of changing censor rates while the cluster sizes and number of individuals remained constant were examined. In this study, 5 different models were used to apply simulated data and AIC, AICc and BIC were used to compare their performances. Results: Within the scope of the findings, the best model based on AIC and AICc is the frailty model. According to the BIC, the best model was obtained as the marginal Cox model. In the simulation studies, the worst model changes in parallel with the increase in the sample size. Conclusion: As a result, ignoring the data structure can lead to biased or inaccurate estimates, especially in health data. In this study, it is recommended to use the frailty or marginal Cox model in clustered survival data depending on the information criteria.