Additive mixed effect model for recurrent gap time data
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Gap times between recurrent events are often of primary interest in medical and observational studies. The additive hazards model, focusing on risk differences rather than risk ratios, has been widely used in practice. However, the marginal additive hazards model does not take the dependence among gap times into account. In this paper, we propose an additive mixed effect model to analyze gap time data, and the proposed model includes a subject-specific random effect to account for the dependence among the gap times. Estimating equation approaches are developed for parameter estimation, and the asymptotic properties of the resulting estimators are established. In addition, some graphical and numerical procedures are presented for model checking. The finite sample behavior of the proposed methods is evaluated through simulation studies, and an application to a data set from a clinic study on chronic granulomatous disease is provided.
KeywordsAdditive mixed effect model Estimating equation Gap times Random effects Recurrent event data
The authors thank the Editor, Professor Mei-Ling Ting Lee, an Associate Editor, and two reviewers for their insightful comments and suggestions that greatly improved the article. This research was partly supported by the National Natural Science Foundation of China Grants (No. 11231010, 11171330 and 11101314), Key Laboratory of RCSDS, CAS (No. 2008DP173182) and BCMIIS.
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