Induced smoothing for the semiparametric accelerated hazards model

Haifen Li, Jiajia Zhang and Yincai Tang

Computational Statistics & Data Analysis, 2012, vol. 56, issue 12, 4312-4319

Abstract: 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.

Keywords: Accelerated hazards model; Rank estimation; Induced smoothing (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947312001491
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:12:p:4312-4319

DOI: 10.1016/j.csda.2012.04.001

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().