Semiparametric Trend Analysis for Stratified Recurrent Gap Times Under Weak Comparability Constraint

Peng Liu (), Yijian Huang (), Kwun Chuen Gary Chan () and Ying Qing Chen ()
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Peng Liu: University of Kent
Yijian Huang: Emory University
Kwun Chuen Gary Chan: University of Washington
Ying Qing Chen: Stanford University

Statistics in Biosciences, 2023, vol. 15, issue 2, No 9, 455-474

Abstract: Abstract Recurrent event data are frequently encountered in many longitudinal studies where each individual may experience more than one event. Wang and Chen (Biometrics 56(3):789–794, 2000) proposed a comparability constraint to estimate the time trend for the gap times, where the gap time pairs that satisfy the constraint have the same conditional distribution. However, the comparable paired gap times are also independent. Therefore, the comparable gap time pairs will be subject to a stronger constraint than needed for the estimation. Thus their procedure is subject to information loss. Under the accelerated failure time model, we propose a new comparability constraint that can overcome the drawback mentioned above. The gap time pairs being selected by the proposed comparability constraint will still have the same distribution, but they do not need to be independent of each other. We showed that the proposed comparability constraint will utilize more gap time data pairs than the strong comparability. And we showed via various simulation studies that the variance will be smaller than Wang and Chen’s (2000) estimator. We apply the proposed method to the HIV Prevention Trial Network 052 study.

Keywords: Accelerated failure time model; Comparability; Gap time; Rank regression; Recurrent event data (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12561-023-09376-8

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