Forward, backward and time-adjusted recurrent event processes in the presence of a failure event
View Presentation *Mei-Cheng Wang, Johns Hopkins Bloomberg School of Public Health Keywords: Backward process, Markers, Recurrent events, Semiparametric models Recurrent events arise in many follow-up and surveillance studies where the observation of recurrent events is terminated by a failure event or censoring. We consider modeling and estimation of recurrent events, possibly together with markers, by forward, backward and failure-time-adjusted process models: (1) Forward recurrent event process starts at a time origin, 0, and moves forward along time t. (2) Backward recurrent event process considers the failure event as the time origin and counts time backward. (3) Failure-time-adjusted process model uses failure-time to adjust recurrent event frequency in modeling. In this talk we will characterize and interpret the three different types of models, discuss statistical challenges for each model, and present some methods and data applications.
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Key Dates
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April 30 - May 22, 2013
Invited Abstract Submission Open -
June 4, 2013
Online Registration Opens -
August 9 - August 23, 2013
Invited Abstract Editing -
August 23, 2013
Short Course materials due from Instructors -
August 26, 2013
Housing Deadline -
September 9, 2013
Cancellation Deadline and Registration Closes @ 11:59 pm EDT -
September 16 - September 18, 2013
Marriott Wardman Park, Washington, DC