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Activity Number: 344 - Semiparametric Modeling
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #330370 Presentation
Title: Semiparametric Trend Analysis for Recurrent Event Data Under Weak Comparability
Author(s): Peng Liu* and Yijian Huang and Kwun Chuen Gary Chan and Ying Qing CHEN
Companies: University of Alberta and Emory University and University of Washington and Fred Hutchinson Cancer Research Center
Keywords: Comparability; Rank regression; Recurrent event data

Recurrent event data are frequently encountered in many clinical trial studies and medical research, where each subject encounters more than one event. A much discussed aspect of recurrent event is the presence or absence of time trend. Trend refers to systematic variation among the occurrence rates of times between events, it can be used as a measure of disease progression. Wang and Chen [Biometrics, 56, 789-794 (2000)] proposed a strong comparability concept to study the trend in recurrent event data. In this paper we propose weak comparability under the same assumption as Wang and Chen (2000). Our proposed concept can produce more comparable pairs and thus result in a more efficient estimate. Monte Carlo simulation as well as real data analyse are performed to validate the effectiveness of the new method.

Authors who are presenting talks have a * after their name.

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