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Semiparametric Trend Analysis For Recurrent Event Data Under Weak Comparability

LIU, Peng and HUANG, Yijian and CHAN, Kwun Chuen Gary and CHEN, Ying Qing (2019) Semiparametric Trend Analysis For Recurrent Event Data Under Weak Comparability. Working paper. Submitted to Biometrics (Submitted) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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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.

Item Type: Monograph (Working paper)
Uncontrolled keywords: Comparability; Rank regression; Recurrent event data.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Peng Liu
Date Deposited: 09 Aug 2019 16:14 UTC
Last Modified: 09 Aug 2019 16:26 UTC
Resource URI: (The current URI for this page, for reference purposes)
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