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Abstract Details
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Activity Number:
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253
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #306683 |
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Title:
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A Piece-Wise Exponential Model to Obtain Inverse Probability Censoring Weights (IPCW)
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Author(s):
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Jie (Rena) Sun*+ and Douglas E Schaubel and John Kalbfleisch
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Companies:
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University of Michigan and University of Michigan and University of Michigan
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Address:
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1420 Washington Heights, Ann Arbor, MI, 48109, United States
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Keywords:
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inverse weights ;
piece-wise exponential model ;
time dependent covariates ;
Cox model
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Abstract:
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Inverse probability censoring weighting (IPCW) technique can be used in the presence of dependent censoring, when time dependent covariates on the causal pathway are predictive of both failure and dependent censoring. This is often the case in medical settings, where preventive therapies are used on patients with worsening prognosis. Much research has been done using a Cox model to obtain such weights. We propose to use a piece-wise exponential model as an alternative, with advantages of gaining computational efficiency in a large dataset and obtaining stable results. Simulation results are shown for demonstration.
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