Additive mixed effect model for recurrent gap time data

• Published in: Lifetime data analysis Vol. 23; no. 2; pp. 223 - 253
• Main Authors: Ding, Jieli, Sun, Liuquan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2017; Springer Nature B.V
• Subjects: Applied mathematics; Biomedical Research; Chronic illnesses; Computer Simulation; Data analysis; Economics; Estimating techniques; Finance; Frailty; Granulomatous Disease, Chronic; Health Sciences; Humans; Insurance; Management; Mathematics and Statistics; Medicine; Models, Statistical; Observational studies; Operations Research/Decision Theory; Parameter estimation; Proportional Hazards Models; Quality Control; Random variables; Reliability; Safety and Risk; Statistics; Statistics as Topic; Statistics for Business; Statistics for Life Sciences; Studies; Time; United States > US; Gap times; Random effects; Additive mixed effect model; Estimating equation; Recurrent event data
• ISSN: 1380-7870; 1572-9249; 1572-9249
• DOI: 10.1007/s10985-015-9341-0

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.