Analysing bivariate survival data with interval sampling and application to cancer epidemiology

Hong Zhu, Mei Cheng Wang

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

In biomedical studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as outcomes to identify the progression of a disease. In cancer studies, interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme, termed interval sampling, in which the first failure event is identified within a calendar time interval, the time of the initiating event can be retrospectively confirmed and the occurrence of the second failure event is observed subject to right censoring. In a cancer data application, the initiating, first and second events could correspond to birth, cancer onset and death. The fact that the data are collected conditional on the first failure event occurring within a time interval induces bias. Interval sampling is widely used for collection of disease registry data by governments and medical institutions, though the interval sampling bias is frequently overlooked by researchers. This paper develops statistical methods for analysing such data. Semiparametric methods are proposed under semi-stationarity and stationarity. Numerical studies demonstrate that the proposed estimation approaches perform well with moderate sample sizes. We apply the proposed methods to ovarian cancer registry data.

Original languageEnglish (US)
Pages (from-to)345-361
Number of pages17
JournalBiometrika
Volume99
Issue number2
DOIs
StatePublished - Jun 2012

Fingerprint

Epidemiology
Survival Data
epidemiology
Cancer
Sampling
neoplasms
Interval
Neoplasms
Stationarity
sampling
Registries
death
Parturition
ovarian neoplasms
Semiparametric Methods
Ovarian Cancer
Right Censoring
Statistical methods
Calendar
disease course

Keywords

  • Bivariate survival distribution
  • Copula
  • Interval sampling
  • Semi-stationarity
  • Semiparametric model
  • Stationarity

ASJC Scopus subject areas

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

Cite this

Analysing bivariate survival data with interval sampling and application to cancer epidemiology. / Zhu, Hong; Wang, Mei Cheng.

In: Biometrika, Vol. 99, No. 2, 06.2012, p. 345-361.

Research output: Contribution to journalArticle

@article{f94c0743535d4f5eb5bafb7d6fa14ecc,
title = "Analysing bivariate survival data with interval sampling and application to cancer epidemiology",
abstract = "In biomedical studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as outcomes to identify the progression of a disease. In cancer studies, interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme, termed interval sampling, in which the first failure event is identified within a calendar time interval, the time of the initiating event can be retrospectively confirmed and the occurrence of the second failure event is observed subject to right censoring. In a cancer data application, the initiating, first and second events could correspond to birth, cancer onset and death. The fact that the data are collected conditional on the first failure event occurring within a time interval induces bias. Interval sampling is widely used for collection of disease registry data by governments and medical institutions, though the interval sampling bias is frequently overlooked by researchers. This paper develops statistical methods for analysing such data. Semiparametric methods are proposed under semi-stationarity and stationarity. Numerical studies demonstrate that the proposed estimation approaches perform well with moderate sample sizes. We apply the proposed methods to ovarian cancer registry data.",
keywords = "Bivariate survival distribution, Copula, Interval sampling, Semi-stationarity, Semiparametric model, Stationarity",
author = "Hong Zhu and Wang, {Mei Cheng}",
year = "2012",
month = "6",
doi = "10.1093/biomet/ass009",
language = "English (US)",
volume = "99",
pages = "345--361",
journal = "Biometrika",
issn = "0006-3444",
publisher = "Oxford University Press",
number = "2",

}

TY - JOUR

T1 - Analysing bivariate survival data with interval sampling and application to cancer epidemiology

AU - Zhu, Hong

AU - Wang, Mei Cheng

PY - 2012/6

Y1 - 2012/6

N2 - In biomedical studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as outcomes to identify the progression of a disease. In cancer studies, interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme, termed interval sampling, in which the first failure event is identified within a calendar time interval, the time of the initiating event can be retrospectively confirmed and the occurrence of the second failure event is observed subject to right censoring. In a cancer data application, the initiating, first and second events could correspond to birth, cancer onset and death. The fact that the data are collected conditional on the first failure event occurring within a time interval induces bias. Interval sampling is widely used for collection of disease registry data by governments and medical institutions, though the interval sampling bias is frequently overlooked by researchers. This paper develops statistical methods for analysing such data. Semiparametric methods are proposed under semi-stationarity and stationarity. Numerical studies demonstrate that the proposed estimation approaches perform well with moderate sample sizes. We apply the proposed methods to ovarian cancer registry data.

AB - In biomedical studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as outcomes to identify the progression of a disease. In cancer studies, interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme, termed interval sampling, in which the first failure event is identified within a calendar time interval, the time of the initiating event can be retrospectively confirmed and the occurrence of the second failure event is observed subject to right censoring. In a cancer data application, the initiating, first and second events could correspond to birth, cancer onset and death. The fact that the data are collected conditional on the first failure event occurring within a time interval induces bias. Interval sampling is widely used for collection of disease registry data by governments and medical institutions, though the interval sampling bias is frequently overlooked by researchers. This paper develops statistical methods for analysing such data. Semiparametric methods are proposed under semi-stationarity and stationarity. Numerical studies demonstrate that the proposed estimation approaches perform well with moderate sample sizes. We apply the proposed methods to ovarian cancer registry data.

KW - Bivariate survival distribution

KW - Copula

KW - Interval sampling

KW - Semi-stationarity

KW - Semiparametric model

KW - Stationarity

UR - http://www.scopus.com/inward/record.url?scp=84861614269&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84861614269&partnerID=8YFLogxK

U2 - 10.1093/biomet/ass009

DO - 10.1093/biomet/ass009

M3 - Article

VL - 99

SP - 345

EP - 361

JO - Biometrika

JF - Biometrika

SN - 0006-3444

IS - 2

ER -