Pseudo-full likelihood estimation for prospective survival analysis with a general semiparametric shared frailty model: Asymptotic theory

David M. Zucker; Malka Gorfine; Li Hsu

doi:10.1016/j.jspi.2007.08.005

Pseudo-full likelihood estimation for prospective survival analysis with a general semiparametric shared frailty model: Asymptotic theory

David M. Zucker^*, Malka Gorfine, Li Hsu

^*Corresponding author for this work

Department of Statistics

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

In this work we present a simple estimation procedure for a general frailty model for analysis of prospective correlated failure times. Earlier work showed this method to perform well in a simulation study. Here we provide rigorous large-sample theory for the proposed estimators of both the regression coefficient vector and the dependence parameter, including consistent variance estimators.

Original language	English
Pages (from-to)	1998-2016
Number of pages	19
Journal	Journal of Statistical Planning and Inference
Volume	138
Issue number	7
DOIs	https://doi.org/10.1016/j.jspi.2007.08.005
State	Published - 1 Jul 2008

Keywords

Correlated failure times
EM algorithm
Frailty model
Prospective family study
Survival analysis

Access to Document

10.1016/j.jspi.2007.08.005

Cite this

@article{c804a084ea7c4930ac5c59ebbae9f177,

title = "Pseudo-full likelihood estimation for prospective survival analysis with a general semiparametric shared frailty model: Asymptotic theory",

abstract = "In this work we present a simple estimation procedure for a general frailty model for analysis of prospective correlated failure times. Earlier work showed this method to perform well in a simulation study. Here we provide rigorous large-sample theory for the proposed estimators of both the regression coefficient vector and the dependence parameter, including consistent variance estimators.",

keywords = "Correlated failure times, EM algorithm, Frailty model, Prospective family study, Survival analysis",

author = "Zucker, \{David M.\} and Malka Gorfine and Li Hsu",

year = "2008",

month = jul,

day = "1",

doi = "10.1016/j.jspi.2007.08.005",

language = "אנגלית",

volume = "138",

pages = "1998--2016",

journal = "Journal of Statistical Planning and Inference",

issn = "0378-3758",

publisher = "Elsevier B.V.",

number = "7",

}

TY - JOUR

T1 - Pseudo-full likelihood estimation for prospective survival analysis with a general semiparametric shared frailty model

T2 - Asymptotic theory

AU - Zucker, David M.

AU - Gorfine, Malka

AU - Hsu, Li

PY - 2008/7/1

Y1 - 2008/7/1

N2 - In this work we present a simple estimation procedure for a general frailty model for analysis of prospective correlated failure times. Earlier work showed this method to perform well in a simulation study. Here we provide rigorous large-sample theory for the proposed estimators of both the regression coefficient vector and the dependence parameter, including consistent variance estimators.

AB - In this work we present a simple estimation procedure for a general frailty model for analysis of prospective correlated failure times. Earlier work showed this method to perform well in a simulation study. Here we provide rigorous large-sample theory for the proposed estimators of both the regression coefficient vector and the dependence parameter, including consistent variance estimators.

KW - Correlated failure times

KW - EM algorithm

KW - Frailty model

KW - Prospective family study

KW - Survival analysis

UR - https://www.scopus.com/pages/publications/41049102693

U2 - 10.1016/j.jspi.2007.08.005

DO - 10.1016/j.jspi.2007.08.005

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

AN - SCOPUS:41049102693

SN - 0378-3758

VL - 138

SP - 1998

EP - 2016

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

IS - 7

ER -

Pseudo-full likelihood estimation for prospective survival analysis with a general semiparametric shared frailty model: Asymptotic theory

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this