Cohort case-control design and analysis for clustered failure-time data

Shou En Lu, Mei Cheng Wang

Research output: Contribution to journalArticle

Abstract

Cohort case-control design is an efficient and economical design to study risk factors for disease incidence or mortality in a large cohort. In the last few decades, a variety of cohort case-control designs have been developed and theoretically justified. These designs have been exclusively applied to the analysis of univariate failure-time data. In this work, a cohort case-control design adapted to multivariate failure-time data is developed. A risk set sampling method is proposed to sample controls from nonfailures in a large cohort for each case matched by failure time. This method leads to a pseudolikelihood approach for the estimation of regression parameters in the marginal proportional hazards model (Cox, 1972, Journal of the Royal Statistical Society, Series B 34, 187-220), where the correlation structure between individuals within a cluster is left unspecified. The performance of the proposed estimator is demonstrated by simulation studies. A bootstrap method is proposed for inferential purposes. This methodology is illustrated by a data example from a child vitamin A supplementation trial in Nepal (Nepal Nutrition Intervention Project-Sarlahi, or NNIPS).

Original languageEnglish (US)
Pages (from-to)764-772
Number of pages9
JournalBiometrics
Volume58
Issue number4
StatePublished - Dec 2002

Fingerprint

Failure Time Data
Clustered Data
Case-control
Control Design
Nepal
Multivariate Failure Times
B-series
Pseudo-likelihood
Cox Proportional Hazards Model
Nutrition
Bootstrap Method
Failure Time
Correlation Structure
Multivariate Data
Sampling Methods
Risk Factors
Vitamin A
Proportional Hazards Models
Mortality
Univariate

Keywords

  • Bootstrap method
  • Clustered failure-time data
  • Cohort case-control study
  • Marginal model
  • Proportional hazards model
  • Pseudolikelihood
  • Time-matched case-control set

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Public Health, Environmental and Occupational Health
  • Agricultural and Biological Sciences (miscellaneous)
  • Applied Mathematics
  • Statistics and Probability

Cite this

Cohort case-control design and analysis for clustered failure-time data. / Lu, Shou En; Wang, Mei Cheng.

In: Biometrics, Vol. 58, No. 4, 12.2002, p. 764-772.

Research output: Contribution to journalArticle

@article{794a8b4a741c418183af5fbed8545e37,
title = "Cohort case-control design and analysis for clustered failure-time data",
abstract = "Cohort case-control design is an efficient and economical design to study risk factors for disease incidence or mortality in a large cohort. In the last few decades, a variety of cohort case-control designs have been developed and theoretically justified. These designs have been exclusively applied to the analysis of univariate failure-time data. In this work, a cohort case-control design adapted to multivariate failure-time data is developed. A risk set sampling method is proposed to sample controls from nonfailures in a large cohort for each case matched by failure time. This method leads to a pseudolikelihood approach for the estimation of regression parameters in the marginal proportional hazards model (Cox, 1972, Journal of the Royal Statistical Society, Series B 34, 187-220), where the correlation structure between individuals within a cluster is left unspecified. The performance of the proposed estimator is demonstrated by simulation studies. A bootstrap method is proposed for inferential purposes. This methodology is illustrated by a data example from a child vitamin A supplementation trial in Nepal (Nepal Nutrition Intervention Project-Sarlahi, or NNIPS).",
keywords = "Bootstrap method, Clustered failure-time data, Cohort case-control study, Marginal model, Proportional hazards model, Pseudolikelihood, Time-matched case-control set",
author = "Lu, {Shou En} and Wang, {Mei Cheng}",
year = "2002",
month = "12",
language = "English (US)",
volume = "58",
pages = "764--772",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "4",

}

TY - JOUR

T1 - Cohort case-control design and analysis for clustered failure-time data

AU - Lu, Shou En

AU - Wang, Mei Cheng

PY - 2002/12

Y1 - 2002/12

N2 - Cohort case-control design is an efficient and economical design to study risk factors for disease incidence or mortality in a large cohort. In the last few decades, a variety of cohort case-control designs have been developed and theoretically justified. These designs have been exclusively applied to the analysis of univariate failure-time data. In this work, a cohort case-control design adapted to multivariate failure-time data is developed. A risk set sampling method is proposed to sample controls from nonfailures in a large cohort for each case matched by failure time. This method leads to a pseudolikelihood approach for the estimation of regression parameters in the marginal proportional hazards model (Cox, 1972, Journal of the Royal Statistical Society, Series B 34, 187-220), where the correlation structure between individuals within a cluster is left unspecified. The performance of the proposed estimator is demonstrated by simulation studies. A bootstrap method is proposed for inferential purposes. This methodology is illustrated by a data example from a child vitamin A supplementation trial in Nepal (Nepal Nutrition Intervention Project-Sarlahi, or NNIPS).

AB - Cohort case-control design is an efficient and economical design to study risk factors for disease incidence or mortality in a large cohort. In the last few decades, a variety of cohort case-control designs have been developed and theoretically justified. These designs have been exclusively applied to the analysis of univariate failure-time data. In this work, a cohort case-control design adapted to multivariate failure-time data is developed. A risk set sampling method is proposed to sample controls from nonfailures in a large cohort for each case matched by failure time. This method leads to a pseudolikelihood approach for the estimation of regression parameters in the marginal proportional hazards model (Cox, 1972, Journal of the Royal Statistical Society, Series B 34, 187-220), where the correlation structure between individuals within a cluster is left unspecified. The performance of the proposed estimator is demonstrated by simulation studies. A bootstrap method is proposed for inferential purposes. This methodology is illustrated by a data example from a child vitamin A supplementation trial in Nepal (Nepal Nutrition Intervention Project-Sarlahi, or NNIPS).

KW - Bootstrap method

KW - Clustered failure-time data

KW - Cohort case-control study

KW - Marginal model

KW - Proportional hazards model

KW - Pseudolikelihood

KW - Time-matched case-control set

UR - http://www.scopus.com/inward/record.url?scp=0036971526&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036971526&partnerID=8YFLogxK

M3 - Article

C2 - 12495130

AN - SCOPUS:0036971526

VL - 58

SP - 764

EP - 772

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 4

ER -