Imputation methods for doubly censored HIV data

Wei Zhang; Ying Zhang; Kathryn Chaloner; Jack T. Stapleton

doi:10.1080/00949650802255618

Imputation methods for doubly censored HIV data

Wei Zhang, Ying Zhang, Kathryn Chaloner, Jack T. Stapleton

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

11 Scopus citations

Abstract

In medical research, it is common to have doubly censored survival data: origin time and event time are both subject to censoring. In this paper, we review simple and probability-based methods that are used to impute interval censored origin time and compare the performance of these methods through extensive simulations in the one-sample problem, two-sample problem and Cox regression model problem. The use of a bootstrap procedure for inference is demonstrated.

Original language	English (US)
Pages (from-to)	1245-1257
Number of pages	13
Journal	Journal of Statistical Computation and Simulation
Volume	79
Issue number	10
DOIs	https://doi.org/10.1080/00949650802255618
State	Published - Oct 2009
Externally published	Yes

Keywords

Bootstrap
Cox regression model
Interval censoring
Kaplan-Meier curve
Logrank test

ASJC Scopus subject areas

Statistics and Probability
Modeling and Simulation
Statistics, Probability and Uncertainty
Applied Mathematics

Access to Document

10.1080/00949650802255618

Cite this

@article{8dd83f53c15b4369a6bed32639290349,

title = "Imputation methods for doubly censored HIV data",

abstract = "In medical research, it is common to have doubly censored survival data: origin time and event time are both subject to censoring. In this paper, we review simple and probability-based methods that are used to impute interval censored origin time and compare the performance of these methods through extensive simulations in the one-sample problem, two-sample problem and Cox regression model problem. The use of a bootstrap procedure for inference is demonstrated.",

keywords = "Bootstrap, Cox regression model, Interval censoring, Kaplan-Meier curve, Logrank test",

author = "Wei Zhang and Ying Zhang and Kathryn Chaloner and Stapleton, {Jack T.}",

note = "Funding Information: This work was supported in part by a Dissertation Fellowship from University of Iowa Graduate College and an NIH/NIAID grant: R01 058740.",

year = "2009",

month = oct,

doi = "10.1080/00949650802255618",

language = "English (US)",

volume = "79",

pages = "1245--1257",

journal = "Journal of Statistical Computation and Simulation",

issn = "0094-9655",

publisher = "Taylor and Francis Ltd.",

number = "10",

}

TY - JOUR

T1 - Imputation methods for doubly censored HIV data

AU - Zhang, Wei

AU - Zhang, Ying

AU - Chaloner, Kathryn

AU - Stapleton, Jack T.

N1 - Funding Information: This work was supported in part by a Dissertation Fellowship from University of Iowa Graduate College and an NIH/NIAID grant: R01 058740.

PY - 2009/10

Y1 - 2009/10

N2 - In medical research, it is common to have doubly censored survival data: origin time and event time are both subject to censoring. In this paper, we review simple and probability-based methods that are used to impute interval censored origin time and compare the performance of these methods through extensive simulations in the one-sample problem, two-sample problem and Cox regression model problem. The use of a bootstrap procedure for inference is demonstrated.

AB - In medical research, it is common to have doubly censored survival data: origin time and event time are both subject to censoring. In this paper, we review simple and probability-based methods that are used to impute interval censored origin time and compare the performance of these methods through extensive simulations in the one-sample problem, two-sample problem and Cox regression model problem. The use of a bootstrap procedure for inference is demonstrated.

KW - Bootstrap

KW - Cox regression model

KW - Interval censoring

KW - Kaplan-Meier curve

KW - Logrank test

UR - http://www.scopus.com/inward/record.url?scp=70350173470&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70350173470&partnerID=8YFLogxK

U2 - 10.1080/00949650802255618

DO - 10.1080/00949650802255618

M3 - Article

C2 - 21304834

AN - SCOPUS:70350173470

SN - 0094-9655

VL - 79

SP - 1245

EP - 1257

JO - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

IS - 10

ER -

Imputation methods for doubly censored HIV data

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this