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Regression analysis of recurrent‐event‐free time from multiple follow‐up windows
Author(s) -
Xia Meng,
Murray Susan,
Tayob Nabihah
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8385
Subject(s) - imputation (statistics) , computer science , event (particle physics) , event data , missing data , regression , regression analysis , statistics , data mining , covariate , mathematics , machine learning , physics , quantum mechanics
This research develops multivariable restricted time models appropriate for analysis of recurrent events data, where data is repurposed into censored longitudinal time‐to‐first‐event outcomes in τ ‐length follow‐up windows. We develop two approaches for addressing the censored nature of the outcomes: a pseudo‐observation (PO) approach and a multiple‐imputation (MI) approach. Each of these approaches allows for complete data methods, such as generalized estimating equations, to be used for the analysis of the newly constructed correlated outcomes. Through simulation, this manuscript assesses the performance of the proposed PO and MI methods. Both PO and MI approaches show attractive results with either correlated or independent gap times in an individual. We also demonstrate how to apply the proposed methods in the data from azithromycin in Chronic Obstructive Pulmonary Disease Trial.