Journal article
A semiparametric approach for the nonparametric transformation survival model with multiple covariates
Biostatistics (Oxford, England), Vol.8(2), pp.197-211
04/2007
DOI: 10.1093/biostatistics/kxl001
PMID: 16670240
Abstract
The nonparametric transformation model makes no parametric assumptions on the forms of the transformation function and the error distribution. This model is appealing in its flexibility for modeling censored survival data. Current approaches for estimation of the regression parameters involve maximizing discontinuous objective functions, which are numerically infeasible to implement with multiple covariates. Based on the partial rank (PR) estimator (Khan and Tamer, 2004), we propose a smoothed PR estimator which maximizes a smooth approximation of the PR objective function. The estimator is shown to be asymptotically equivalent to the PR estimator but is much easier to compute when there are multiple covariates. We further propose using the weighted bootstrap, which is more stable than the usual sandwich technique with smoothing parameters, for estimating the standard error. The estimator is evaluated via simulation studies and illustrated with the Veterans Administration lung cancer data set.
Details
- Title: Subtitle
- A semiparametric approach for the nonparametric transformation survival model with multiple covariates
- Creators
- Xiao Song - Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. songx@u.washington.eduShuangge MaJian HuangXiao-Hua Zhou
- Resource Type
- Journal article
- Publication Details
- Biostatistics (Oxford, England), Vol.8(2), pp.197-211
- DOI
- 10.1093/biostatistics/kxl001
- PMID
- 16670240
- NLM abbreviation
- Biostatistics
- ISSN
- 1465-4644
- eISSN
- 1468-4357
- Publisher
- England
- Grant note
- HL72288 / NHLBI NIH HHS N01 HC95159 / NHLBI NIH HHS
- Language
- English
- Date published
- 04/2007
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9983985957602771
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