Abstract
We propose a new parametric time-varying shared frailty model to represent changes over time in population heterogeneity, for use with bivariate current status data. The model uses a power transformation of a time-invariant frailty $U$, and is particularly convenient when $U$ is a member of the generalized gamma family. This model avoids some shortcomings of a previously suggested time-varying frailty model, notably time-dependent support. We describe some key properties of the model, including its relative frailty variance function in different settings and how the model can be fitted to data. We describe several applications to shared frailty modeling of bivariate current status data on infectious diseases, in which the frailty represents age-dependent heterogeneity in contact rates or susceptibility to infection.
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