Accelerated Recurrence Time Models

Event Details
  • Date/Time:
    • Thursday November 15, 2007
      10:00 am - 11:00 am
  • Location: ISyE MAIN, Executive Classroom, # 228
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Contact
Xiaoming Huo
ISyE
Contact Xiaoming Huo
404-894-2300
Summaries

Summary Sentence: ISyE Statistics Series

Full Summary: Dr. Yijian Huang Associate Professor Department of Biostatistics Rollins School of Public Health Emory University

Dr. Yijian Huang
Associate Professor
Department of Biostatistics
Rollins School of Public Health
Emory University

Accelerated Recurrence Time Models

For the analysis with recurrent events, we propose a generalization of the accelerated failure time model to allow for evolving covariate effects. These so-called accelerated recurrence time models postulate that time to expected recurrence frequency, upon transformation, is a linear function of covariates with frequency-dependent coefficients. This modeling strategy shares the same spirit as quantile regression. An estimation and inference procedure is developed by generalizing the celebrated Powell's (1984, 1986) estimator for censored quantile regression. Consistency and asymptotic normality of the proposed estimator are established. An algorithm is devised to attain good computational efficiency. Simulations demonstrate that this proposal performs well under practical settings. This methodology is illustrated in an application to the well-known bladder cancer study.

This talk is based on joint work with Limin Peng.

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H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

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Seminar/Lecture/Colloquium
Keywords
ISyE Stewart School, Statistics Seminar
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  • Created By: Ruth Gregory
  • Workflow Status: Published
  • Created On: Oct 12, 2009 - 5:21pm
  • Last Updated: Oct 7, 2016 - 9:48pm