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Wiest, L., Buleté, A., Giroud, B., Fratta, C., Amic, S., Lambert, O., Pouliquen, H. and Arnaudguilhem, C. (2011) Multi-Residue Analysis of 80 Environmental Contaminants in Honeys, Honeybees and Pollens by One Extraction Procedure Followed by Liquid and Gas Chromatography Coupled with Mass Spectrometric Detection. Journal of Chromatography A, 1218, 5743-5756.
https://doi.org/10.1016/j.chroma.2011.06.079

has been cited by the following article:

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

  AUTHORS: Patience Isiaho, Daisy Salifu, Samuel Mwalili, Henri E. Z. Tonnang

  KEYWORDS: Correlated Failure Times, Frailty, Survival Analysis, Unobserved Heterogeneity

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.12 No.2, May 29, 2024

  ABSTRACT: 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.