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Bayesian nested frailty model for evaluating surgical management of patulous Eustachian tube dysfunction
Kosuke Kawai1, Bryan K Ward2, Joonas Toivonen3,4
1Department of Medicine Statistics Core, David Geffen School of Medicine, University of California los Angeles, 1100 Glendon Avenue, Los Angeles, CA, 90024, USA. kkawai@mednet.ucla.edu.
Background:
The nested frailty model, a random effects survival model that can accommodate data clustered at two hierarchical levels, has been rarely used in practice. We aimed to evaluate the utility of the Bayesian nested frailty modeling approach in the context of a study to examine the effects of various surgical procedures for patients with patulous Eustachian tube dysfunction (PETD).
Methods:
A nested frailty model was employed to account for the correlation between each pair of ears within patients and the correlation between multiple event times within each ear. Some patients underwent multiple different surgical treatments in their affected ears. We incorporated two nested lognormal frailties into the Cox proportional hazards model. The Bayesian Monte Carlo Markov Chain approach was utilized. We examined the consequences of ignoring a multilevel structure of the data.
Results:
The variances of patient-level and ear-level random effects were both found to be significant in the nested frailty model. Shim insertion and patulous Eustachian tube reconstruction using Alloderm or cartilage were associated with a lower risk of recurrence of PETD symptoms than calcium hydroxyapatite injection.
Conclusions:
Bayesian nested frailty models provide flexibility in modeling hierarchical survival data and effectively account for multiple levels of clustering. Our study highlights the importance of accounting for all levels of hierarchical clustering for valid inference.
