Weighted estimating equations for semiparametric transformation models with censored data from a case-cohort design

Lan Kong, Jianwen Cai, Pranab K. Sen

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

In a case-cohort design introduced by Prentice (1986), covariates are assembled only for a subcohort randomly selected from the entire cohort, and any additional cases outside the subcohort. Semiparametric transformation models are considered here for failure time data from the case-cohort design. Weighted estimating equations are proposed for estimation of the regression parameters. The estimation procedure of survival probability at given covariate levels is also provided. Asymptotic properties are derived for the estimators using finite population sampling theory, U-statistics theory and martingale convergence results. The finite-sample properties of the proposed estimators, as well as the efficiency relative to the full cohort estimators, are assessed via simulation studies. A case-cohort dataset from the Atherosclerosis Risk in Communities study is used to illustrate the estimating procedure.

Original languageEnglish (US)
Pages (from-to)305-319
Number of pages15
JournalBiometrika
Volume91
Issue number2
DOIs
StatePublished - Dec 1 2004

Fingerprint

Case-cohort Design
Weighted Estimating Equations
Transformation Model
Semiparametric Model
Censored Data
Estimator
Covariates
Population Dynamics
atherosclerosis
Finite Population Sampling
Atherosclerosis
Sampling Theory
Failure Time Data
U-statistics
statistics
Relative Efficiency
Survival Probability
Statistics
Sampling
Martingale

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

@article{64df2b10fa734b68af18bbe202093204,
title = "Weighted estimating equations for semiparametric transformation models with censored data from a case-cohort design",
abstract = "In a case-cohort design introduced by Prentice (1986), covariates are assembled only for a subcohort randomly selected from the entire cohort, and any additional cases outside the subcohort. Semiparametric transformation models are considered here for failure time data from the case-cohort design. Weighted estimating equations are proposed for estimation of the regression parameters. The estimation procedure of survival probability at given covariate levels is also provided. Asymptotic properties are derived for the estimators using finite population sampling theory, U-statistics theory and martingale convergence results. The finite-sample properties of the proposed estimators, as well as the efficiency relative to the full cohort estimators, are assessed via simulation studies. A case-cohort dataset from the Atherosclerosis Risk in Communities study is used to illustrate the estimating procedure.",
author = "Lan Kong and Jianwen Cai and Sen, {Pranab K.}",
year = "2004",
month = "12",
day = "1",
doi = "10.1093/biomet/91.2.305",
language = "English (US)",
volume = "91",
pages = "305--319",
journal = "Biometrika",
issn = "0006-3444",
publisher = "Oxford University Press",
number = "2",

}

Weighted estimating equations for semiparametric transformation models with censored data from a case-cohort design. / Kong, Lan; Cai, Jianwen; Sen, Pranab K.

In: Biometrika, Vol. 91, No. 2, 01.12.2004, p. 305-319.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Weighted estimating equations for semiparametric transformation models with censored data from a case-cohort design

AU - Kong, Lan

AU - Cai, Jianwen

AU - Sen, Pranab K.

PY - 2004/12/1

Y1 - 2004/12/1

N2 - In a case-cohort design introduced by Prentice (1986), covariates are assembled only for a subcohort randomly selected from the entire cohort, and any additional cases outside the subcohort. Semiparametric transformation models are considered here for failure time data from the case-cohort design. Weighted estimating equations are proposed for estimation of the regression parameters. The estimation procedure of survival probability at given covariate levels is also provided. Asymptotic properties are derived for the estimators using finite population sampling theory, U-statistics theory and martingale convergence results. The finite-sample properties of the proposed estimators, as well as the efficiency relative to the full cohort estimators, are assessed via simulation studies. A case-cohort dataset from the Atherosclerosis Risk in Communities study is used to illustrate the estimating procedure.

AB - In a case-cohort design introduced by Prentice (1986), covariates are assembled only for a subcohort randomly selected from the entire cohort, and any additional cases outside the subcohort. Semiparametric transformation models are considered here for failure time data from the case-cohort design. Weighted estimating equations are proposed for estimation of the regression parameters. The estimation procedure of survival probability at given covariate levels is also provided. Asymptotic properties are derived for the estimators using finite population sampling theory, U-statistics theory and martingale convergence results. The finite-sample properties of the proposed estimators, as well as the efficiency relative to the full cohort estimators, are assessed via simulation studies. A case-cohort dataset from the Atherosclerosis Risk in Communities study is used to illustrate the estimating procedure.

UR - http://www.scopus.com/inward/record.url?scp=20444485288&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=20444485288&partnerID=8YFLogxK

U2 - 10.1093/biomet/91.2.305

DO - 10.1093/biomet/91.2.305

M3 - Article

VL - 91

SP - 305

EP - 319

JO - Biometrika

JF - Biometrika

SN - 0006-3444

IS - 2

ER -