Literature DB >> 17786554

Variable selection strategies in survival models with multiple imputations.

Filia Vonta1, Alex Karagrigoriou.   

Abstract

In this paper, the variable selection strategies (criteria) are thoroughly discussed and their use in various survival models is investigated. The asymptotic efficiency property, in the sense of Shibata Ann Stat 8: 147-164, 1980, of a class of variable selection strategies which includes the AIC and all criteria equivalent to it, is established for a general class of survival models, such as parametric frailty or transformation models and accelerated failure time models, under minimum conditions. Furthermore, a multiple imputations method is proposed which is found to successfully handle censored observations and constitutes a competitor to existing methods in the literature. A number of real and simulated data are used for illustrative purposes.

Mesh:

Year:  2007        PMID: 17786554     DOI: 10.1007/s10985-007-9050-4

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  8 in total

1.  Multiple imputation of missing blood pressure covariates in survival analysis.

Authors:  S van Buuren; H C Boshuizen; D L Knook
Journal:  Stat Med       Date:  1999-03-30       Impact factor: 2.373

2.  A multiple imputation approach to regression analysis for doubly censored data with application to AIDS studies.

Authors:  W Pan
Journal:  Biometrics       Date:  2001-12       Impact factor: 2.571

3.  Bootstrap choice of estimators in parametric and semiparametric families: an extension of EIC.

Authors:  B Liquet; C Sakarovitch; D Commenges
Journal:  Biometrics       Date:  2003-03       Impact factor: 2.571

4.  Survival analysis using auxiliary variables via non-parametric multiple imputation.

Authors:  Chiu-Hsieh Hsu; Jeremy M G Taylor; Susan Murray; Daniel Commenges
Journal:  Stat Med       Date:  2006-10-30       Impact factor: 2.373

5.  Variable selection for logistic regression using a prediction-focused information criterion.

Authors:  Gerda Claeskens; Christophe Croux; Johan Van Kerckhoven
Journal:  Biometrics       Date:  2006-12       Impact factor: 2.571

6.  Multiple imputation for interval censored data with auxiliary variables.

Authors:  Chiu-Hsieh Hsu; Jeremy M G Taylor; Susan Murray; Daniel Commenges
Journal:  Stat Med       Date:  2007-02-20       Impact factor: 2.373

7.  The impact of heterogeneity in individual frailty on the dynamics of mortality.

Authors:  J W Vaupel; K G Manton; E Stallard
Journal:  Demography       Date:  1979-08

8.  Analysis of survival data by the proportional odds model.

Authors:  S Bennett
Journal:  Stat Med       Date:  1983 Apr-Jun       Impact factor: 2.373

  8 in total
  1 in total

1.  A simple prognostic index based on admission vital signs data among patients with sepsis in a resource-limited setting.

Authors:  Stephen B Asiimwe; Amir Abdallah; Richard Ssekitoleko
Journal:  Crit Care       Date:  2015-03-16       Impact factor: 9.097

  1 in total

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