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Abstract Details
Activity Number:
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596
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Type:
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Invited
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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WNAR
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Abstract - #300374 |
Title:
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An Accelerated-Proportional-Additive Regression Model of Mean Residual Life with an Event-Free Fraction for Censored Data
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Author(s):
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Chen-Hsin Chen*+ and Wei-Hwa Chang
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Companies:
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Academia Sinica and Academia Sinica
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Address:
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Institute of Statistical Science, Taipei, International, 11529, Taiwan, R.O.C.
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Keywords:
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Cure model ;
Life expectancy ;
Logistic model ;
Martingale process ;
Mixture model ;
Residual life
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Abstract:
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In demographical and medical studies, it is customary to predict a surviving subject's life expectancy using the mean residual life function. To evaluate the covariate effects on the residual life with censored data, the proportional mean residual life model (Chen & Cheng, 2005), the linear mean residual life model (Chen & Cheng, 2006), and other models have been recently developed. Chen & Cheng (2006) noticed a critical challenge for estimating the mean residual life function when the estimated event time distribution has a level-off tail. In this case, the measure of life expectancy becomes meaningless in the whole study population and makes sense only in the susceptible or non-cured subpopulation. To cope with this phenomenon, we propose a semiparametric mixture model (Kuk & Chen, 1992; Lu & Ying, 2004) combining the logistic model for the event probability with a class of accelerated-proportional-additive mean residual life models. We also provide two useful quantities in predicting the life expectancy for a surviving subject. Inference procedures are developed to take into account of censoring, and also evaluated via simulation studies and an illustrative data analysis.
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Authors who are presenting talks have a * after their name.
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