Proportional mean residual life model for right-censored length-biased data

To study disease association with risk factors in epidemiologie studies, cross-sectional sampling is often more focused and less costly for recruiting study subjects who have already experienced initiating events. For time-to-event outcome, however, such a sampling strategy may be length biased. Cou...

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Bibliographic Details
Published in:Biometrika 2012-12, Vol.99 (4), p.995-1000
Main Authors: CHAN, KWUN CHUEN GARY, CHEN, YING QING, DI, CHONG-ZHI
Format: Article
Language:English
Subjects:
Applications
Biology, psychology, social sciences
Censored data
Censorship
Correlation analysis
Epidemiology
Estimation bias
Estimation methods
Estimators
Exact sciences and technology
General topics
Mathematical models
Mathematics
Miscellanea
Modeling
Multivariate analysis
Nonparametric inference
Parametric models
Probability and statistics
Regression analysis
Residual neoplasm
Risk factors
Sampling bias
Sampling techniques
Sciences and techniques of general use
Statistics
Studies
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Summary:To study disease association with risk factors in epidemiologie studies, cross-sectional sampling is often more focused and less costly for recruiting study subjects who have already experienced initiating events. For time-to-event outcome, however, such a sampling strategy may be length biased. Coupled with censoring, analysis of length-biased data can be quite challenging, due to induced informative censoring in which the survival time and censoring time are correlated through a common backward recurrence time. We propose to use the proportional mean residual life model of Oakes & Dasu (Biometrika 77, 409-10, 1990) for analysis of censored length-biased survival data. Several nonstandard data structures, including censoring of onset time and cross-sectional data without follow-up, can also be handled by the proposed methodology.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/ass049