Summary We propose an additive mixed effect model to analyze clustered failure time data. The proposed model assumes an additive structure and includes a random effect as an additional component. Our model imitates the commonly used mixed effect models in
repeated measurement analysis but under the context of hazards regression; our model can also be considered as a parallel development of the gamma‐frailty model in additive model structures. We develop estimating equations for parameter estimation and propose a way of assessing the
distribution of the latent random effect in the presence of large clusters. We establish the asymptotic properties of the proposed estimator. The small sample performance of our method is demonstrated via a large number of simulation studies. Finally, we apply the proposed model to analyze
data from a diabetic study and a treatment trial for congestive heart failure.
No Supplementary Data
Document Type: Research Article
Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina 27599-7420, U.S.A.
Publication date: 2011-12-01