The correlated and shared gamma frailty model for bivariate current status data: an illustration for cross-sectional serological data.
Frailty models are often used to study the individual heterogeneity in multivariate survival analysis. Whereas the shared frailty model is widely applied, the correlated frailty model has gained attention because it elevates the restriction of unobserved factors to act similar within clusters. Estimating frailty models is not straightforward due to various types of censoring. In this paper, we study the behavior of the bivariate-correlated gamma frailty model for type I interval-censored data, better known as current status data. We show that applying a shared rather than a correlated frailty model to cross-sectionally collected serological data on hepatitis A and B leads to biased estimates for the baseline hazard and variance parameters.
Hens
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N
Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Limburg, Belgium. niel.hens@uhasselt.be
Wienke
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A
Aerts
M
M
Molenberghs
G
G
D003141:Communicable Diseases; D003430:Cross-Sectional Studies; D006801:Humans; D015233:Models, Statistical; D009107:Mumps; D012409:Rubella; D012699:Serology / Q000706:statistics & numerical data*; D016019:Survival Analysis