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Induced Smoothing for the Semiparametric Accelerated Hazards Model.
MedLine Citation:
PMID:  23049151     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Compared to the proportional hazards model and accelerated failure time model, the accelerated hazards model has a unique property in its application, in that it can allow gradual effects of the treatment. However, its application is still very limited, partly due to the complexity of existing semiparametric estimation methods. We propose a new semiparametric estimation method based on the induced smoothing and rank type estimates. The parameter estimates and their variances can be easily obtained from the smoothed estimating equation; thus it is easy to use in practice. Our numerical study shows that the new method is more efficient than the existing methods with respect to its variance estimation and coverage probability. The proposed method is employed to reanalyze a data set from a brain tumor treatment study.
Authors:
Haifen Li; Jiajia Zhang; Yincai Tang
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-4-9
Journal Detail:
Title:  Computational statistics & data analysis     Volume:  56     ISSN:  0167-9473     ISO Abbreviation:  Comput Stat Data Anal     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-10-10     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100960938     Medline TA:  Comput Stat Data Anal     Country:  -    
Other Details:
Languages:  ENG     Pagination:  4312-4319     Citation Subset:  -    
Affiliation:
School of Finance and Statistics, East China Normal University, Shanghai, 200241, P. R. China ; Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA.
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