This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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360
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #308524 |
Title:
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Proportional Hazards Regression for the Analysis of Failure Time Data with Outcome-Dependent Sampling and Dependent Censoring
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Author(s):
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Hui Zhang*+ and John David Kalbfleisch and Douglas E. Schaubel
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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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3775 Green Brier Blvd Apt 252B, Ann Arbor, MI, 48105,
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Keywords:
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Cox model ;
Dependent censoring ;
Outcome-dependent sampling ;
Survival analysis
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Abstract:
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Outcome-dependent sampling (ODS) is an efficient and cost-saving sampling scheme, wherein subjects selected into the study based on the outcomes of interest (i.e., death, survival). Most methods for analyzing ODS-based data have an underlying assumption that subjects are censored in a manner independent of the failure rate; an assumption which is often violated. For example, renal failure patients may withdraw from dialysis due to failing health, an issue which could produce substantial bias if ignored. In this paper, we develop a novel double-inverse-weighting scheme which combines weights corresponding to the probability of remaining uncensored and the probability of being sampled. The proposed estimators of the regression parameter are shown to be consistent and asymptotically normal. The proposed methods are applied to the Dialysis Outcomes and Practice Patterns Study (DOPPS) data.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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