Multiple imputation for simple estimation of the hazard function based on interval censored data

Judith D. Bebchuk, Rebecca Betensky

Research output: Contribution to journalArticle

Abstract

A data augmentation algorithm is presented for estimating the hazard function and pointwise variability intervals based on interval censored data. The algorithm extends that proposed by Tanner and Wong for grouped right censored data to interval censored data. It applies multiple imputation and local likelihood methods to obtain smooth non-parametric estimates for the hazard function. This approach considerably simplifies the problem of estimation for interval censored data as it transforms it into the more tractable problem of estimation for right censored data. The method is illustrated for two real data sets: times to breast cosmesis deterioration and times to HIV-1 infection for individuals with haemophilia. Simulations are presented to assess the effects of various parameters on the estimates and their variances. Copyright (C) 2000 John Wiley and Sons, Ltd.

Original languageEnglish (US)
Pages (from-to)405-419
Number of pages15
JournalStatistics in Medicine
Volume19
Issue number3
DOIs
StatePublished - Feb 15 2000

Fingerprint

Interval-censored Data
Multiple Imputation
Hazard Function
Right-censored Data
Hemophilia A
Local Likelihood
HIV Infections
Grouped Data
HIV-1
Data Augmentation
Breast
Likelihood Methods
Deterioration
Estimate
Infection
Simplify
Transform
Interval
Simulation
Datasets

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Cite this

Multiple imputation for simple estimation of the hazard function based on interval censored data. / Bebchuk, Judith D.; Betensky, Rebecca.

In: Statistics in Medicine, Vol. 19, No. 3, 15.02.2000, p. 405-419.

Research output: Contribution to journalArticle

@article{136a84f730934cc5be9454bff6728443,
title = "Multiple imputation for simple estimation of the hazard function based on interval censored data",
abstract = "A data augmentation algorithm is presented for estimating the hazard function and pointwise variability intervals based on interval censored data. The algorithm extends that proposed by Tanner and Wong for grouped right censored data to interval censored data. It applies multiple imputation and local likelihood methods to obtain smooth non-parametric estimates for the hazard function. This approach considerably simplifies the problem of estimation for interval censored data as it transforms it into the more tractable problem of estimation for right censored data. The method is illustrated for two real data sets: times to breast cosmesis deterioration and times to HIV-1 infection for individuals with haemophilia. Simulations are presented to assess the effects of various parameters on the estimates and their variances. Copyright (C) 2000 John Wiley and Sons, Ltd.",
author = "Bebchuk, {Judith D.} and Rebecca Betensky",
year = "2000",
month = "2",
day = "15",
doi = "10.1002/(SICI)1097-0258(20000215)19:3<405::AID-SIM325>3.0.CO;2-2",
language = "English (US)",
volume = "19",
pages = "405--419",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "3",

}

TY - JOUR

T1 - Multiple imputation for simple estimation of the hazard function based on interval censored data

AU - Bebchuk, Judith D.

AU - Betensky, Rebecca

PY - 2000/2/15

Y1 - 2000/2/15

N2 - A data augmentation algorithm is presented for estimating the hazard function and pointwise variability intervals based on interval censored data. The algorithm extends that proposed by Tanner and Wong for grouped right censored data to interval censored data. It applies multiple imputation and local likelihood methods to obtain smooth non-parametric estimates for the hazard function. This approach considerably simplifies the problem of estimation for interval censored data as it transforms it into the more tractable problem of estimation for right censored data. The method is illustrated for two real data sets: times to breast cosmesis deterioration and times to HIV-1 infection for individuals with haemophilia. Simulations are presented to assess the effects of various parameters on the estimates and their variances. Copyright (C) 2000 John Wiley and Sons, Ltd.

AB - A data augmentation algorithm is presented for estimating the hazard function and pointwise variability intervals based on interval censored data. The algorithm extends that proposed by Tanner and Wong for grouped right censored data to interval censored data. It applies multiple imputation and local likelihood methods to obtain smooth non-parametric estimates for the hazard function. This approach considerably simplifies the problem of estimation for interval censored data as it transforms it into the more tractable problem of estimation for right censored data. The method is illustrated for two real data sets: times to breast cosmesis deterioration and times to HIV-1 infection for individuals with haemophilia. Simulations are presented to assess the effects of various parameters on the estimates and their variances. Copyright (C) 2000 John Wiley and Sons, Ltd.

UR - http://www.scopus.com/inward/record.url?scp=0034651818&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034651818&partnerID=8YFLogxK

U2 - 10.1002/(SICI)1097-0258(20000215)19:3<405::AID-SIM325>3.0.CO;2-2

DO - 10.1002/(SICI)1097-0258(20000215)19:3<405::AID-SIM325>3.0.CO;2-2

M3 - Article

VL - 19

SP - 405

EP - 419

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 3

ER -