Abstract
Shared frailty explains correlations between subjects within clusters. However, it does have some limitations. First, it forces the unobserved factors to be the same within the cluster, which may not always reflect reality. For example, at times, it may be inappropriate to assume that all partners in a cluster share all their unobserved risk factors. Second, the dependence between survival times within the cluster is based on marginal distributions of survival times. However, when covariates are present in a proportional hazards model with gamma-distributed frailty, the dependence parameter and the population heterogeneity are confounded (Clayton and Cuzick 1984)
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Hanagal, D.D. (2019). Correlated Gamma and Inverse Gaussian Frailty Models. In: Modeling Survival Data Using Frailty Models. Industrial and Applied Mathematics. Springer, Singapore. https://doi.org/10.1007/978-981-15-1181-3_13
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DOI: https://doi.org/10.1007/978-981-15-1181-3_13
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