Semiparametric Failure Time Regression With Replicates of Mismeasured Covariates

Chengcheng Hu, D. Y. Lin

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

This article proposes a general strategy for the regression analysis of univariate and multivariate failure time data when a subset of covariates cannot be measured precisely but replicate measurements of their surrogates are available. Multivariate failure time data include recurrent events and clustered survival data. The number of replicate measurements can vary from subject to subject and can even depend on the failure time. No parametric assumption is imposed on the error or on any other random variable. Several semiparametric regression models are considered, including the Cox proportional hazards model for univariate failure time data, multiplicative intensity/rate models for recurrent events data, and marginal Cox proportional hazards models for general multivariate failure time data. The existing estimating functions in the absence of measurement error are corrected to yield consistent and asymptotically normal estimators of the regression parameters. The estimation of the underlying failure time distribution is also studied. The operating characteristics of the proposed estimators are assessed through extensive simulation studies. An application to multiple tumor recurrences from a cancer prevention trial is provided.

Original languageEnglish (US)
Pages (from-to)105-118
Number of pages14
JournalJournal. American Statistical Association
Volume99
Issue number465
StatePublished - Mar 2004
Externally publishedYes

Fingerprint

Failure Time Data
Failure Time
Multivariate Failure Times
Covariates
Multivariate Data
Regression
Recurrent Events
Cox Proportional Hazards Model
Univariate
Semiparametric Regression Model
Estimator
Clustered Data
Estimating Function
Survival Data
Operating Characteristics
Regression Analysis
Measurement Error
Recurrence
Tumor
Multiplicative

Keywords

  • Censoring
  • Cox regression
  • Marginal model
  • Measurement error
  • Multivariate failure time
  • Survival data

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

Cite this

Semiparametric Failure Time Regression With Replicates of Mismeasured Covariates. / Hu, Chengcheng; Lin, D. Y.

In: Journal. American Statistical Association, Vol. 99, No. 465, 03.2004, p. 105-118.

Research output: Contribution to journalArticle

@article{b9cf9f9c0e2a4a8f88e8b529c5aa9be3,
title = "Semiparametric Failure Time Regression With Replicates of Mismeasured Covariates",
abstract = "This article proposes a general strategy for the regression analysis of univariate and multivariate failure time data when a subset of covariates cannot be measured precisely but replicate measurements of their surrogates are available. Multivariate failure time data include recurrent events and clustered survival data. The number of replicate measurements can vary from subject to subject and can even depend on the failure time. No parametric assumption is imposed on the error or on any other random variable. Several semiparametric regression models are considered, including the Cox proportional hazards model for univariate failure time data, multiplicative intensity/rate models for recurrent events data, and marginal Cox proportional hazards models for general multivariate failure time data. The existing estimating functions in the absence of measurement error are corrected to yield consistent and asymptotically normal estimators of the regression parameters. The estimation of the underlying failure time distribution is also studied. The operating characteristics of the proposed estimators are assessed through extensive simulation studies. An application to multiple tumor recurrences from a cancer prevention trial is provided.",
keywords = "Censoring, Cox regression, Marginal model, Measurement error, Multivariate failure time, Survival data",
author = "Chengcheng Hu and Lin, {D. Y.}",
year = "2004",
month = "3",
language = "English (US)",
volume = "99",
pages = "105--118",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor and Francis Ltd.",
number = "465",

}

TY - JOUR

T1 - Semiparametric Failure Time Regression With Replicates of Mismeasured Covariates

AU - Hu, Chengcheng

AU - Lin, D. Y.

PY - 2004/3

Y1 - 2004/3

N2 - This article proposes a general strategy for the regression analysis of univariate and multivariate failure time data when a subset of covariates cannot be measured precisely but replicate measurements of their surrogates are available. Multivariate failure time data include recurrent events and clustered survival data. The number of replicate measurements can vary from subject to subject and can even depend on the failure time. No parametric assumption is imposed on the error or on any other random variable. Several semiparametric regression models are considered, including the Cox proportional hazards model for univariate failure time data, multiplicative intensity/rate models for recurrent events data, and marginal Cox proportional hazards models for general multivariate failure time data. The existing estimating functions in the absence of measurement error are corrected to yield consistent and asymptotically normal estimators of the regression parameters. The estimation of the underlying failure time distribution is also studied. The operating characteristics of the proposed estimators are assessed through extensive simulation studies. An application to multiple tumor recurrences from a cancer prevention trial is provided.

AB - This article proposes a general strategy for the regression analysis of univariate and multivariate failure time data when a subset of covariates cannot be measured precisely but replicate measurements of their surrogates are available. Multivariate failure time data include recurrent events and clustered survival data. The number of replicate measurements can vary from subject to subject and can even depend on the failure time. No parametric assumption is imposed on the error or on any other random variable. Several semiparametric regression models are considered, including the Cox proportional hazards model for univariate failure time data, multiplicative intensity/rate models for recurrent events data, and marginal Cox proportional hazards models for general multivariate failure time data. The existing estimating functions in the absence of measurement error are corrected to yield consistent and asymptotically normal estimators of the regression parameters. The estimation of the underlying failure time distribution is also studied. The operating characteristics of the proposed estimators are assessed through extensive simulation studies. An application to multiple tumor recurrences from a cancer prevention trial is provided.

KW - Censoring

KW - Cox regression

KW - Marginal model

KW - Measurement error

KW - Multivariate failure time

KW - Survival data

UR - http://www.scopus.com/inward/record.url?scp=2142837131&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=2142837131&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:2142837131

VL - 99

SP - 105

EP - 118

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 0162-1459

IS - 465

ER -