Comparison of Semi-Parametric Shared Frailty Models for Bees’Survival  

作　　者：Patience Isiaho Daisy Salifu Samuel Mwalili Henri E. Z. Tonnang Patience Isiaho;Daisy Salifu;Samuel Mwalili;Henri E. Z. Tonnang(Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya;Data Management, Modelling, and Geo-Information Unit, International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya)

机构地区：[1]Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya [2]Data Management, Modelling, and Geo-Information Unit, International Centre of Insect Physiology and Ecology (ICIPE), Nairobi, Kenya

出　　处：《Journal of Data Analysis and Information Processing》2024年第2期267-288,共22页数据分析和信息处理（英文）

摘　　要：Survival analysis is a fundamental tool in medical science for time-to-event data. However, its application to colony organisms like bees poses challenges due to their social nature. Traditional survival models may not accurately capture the interdependence among individuals within a colony. Frailty models, accounting for shared risks within groups, offer a promising alternative. This study evaluates the performance of semi-parametric shared frailty models (gamma, inverse normal, and positive stable-in comparison to the traditional Cox model using bees’ survival data). We examined the effect of misspecification of the frailty distribution on regression and heterogeneity parameters using simulation and concluded that the heterogeneity parameter was more sensitive to misspecification of the frailty distribution and choice of initial parameters (cluster size and true heterogeneity parameter) compared to the regression parameter. From the data, parameter estimates for covariates were close for the four models but slightly higher for the Cox model. The shared gamma frailty model provided a better fit to the data in comparison with the other models. Therefore, when focusing on regression parameters, the gamma frailty model is recommended. This research underscores the importance of tailored survival methodologies for accurately analyzing time-to-event data in social organisms.Survival analysis is a fundamental tool in medical science for time-to-event data. However, its application to colony organisms like bees poses challenges due to their social nature. Traditional survival models may not accurately capture the interdependence among individuals within a colony. Frailty models, accounting for shared risks within groups, offer a promising alternative. This study evaluates the performance of semi-parametric shared frailty models (gamma, inverse normal, and positive stable-in comparison to the traditional Cox model using bees’ survival data). We examined the effect of misspecification of the frailty distribution on regression and heterogeneity parameters using simulation and concluded that the heterogeneity parameter was more sensitive to misspecification of the frailty distribution and choice of initial parameters (cluster size and true heterogeneity parameter) compared to the regression parameter. From the data, parameter estimates for covariates were close for the four models but slightly higher for the Cox model. The shared gamma frailty model provided a better fit to the data in comparison with the other models. Therefore, when focusing on regression parameters, the gamma frailty model is recommended. This research underscores the importance of tailored survival methodologies for accurately analyzing time-to-event data in social organisms.

关 键 词：Correlated Failure Times FRAILTY Survival Analysis Unobserved Heterogeneity 

分 类 号：R73[医药卫生—肿瘤]