Multi-state models for colon cancer recurrence and death with a cured fraction

A. S C Conlon, J. M G Taylor, D. J. Sargent

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

In cancer clinical trials, patients often experience a recurrence of disease prior to the outcome of interest, overall survival. Additionally, for many cancers, there is a cured fraction of the population who will never experience a recurrence. There is often interest in how different covariates affect the probability of being cured of disease and the time to recurrence, time to death, and time to death after recurrence. We propose a multi-state Markov model with an incorporated cured fraction to jointly model recurrence and death in colon cancer. A Bayesian estimation strategy is used to obtain parameter estimates. The model can be used to assess how individual covariates affect the probability of being cured and each of the transition rates. Checks for the adequacy of the model fit and for the functional forms of covariates are explored. The methods are applied to data from 12 randomized trials in colon cancer, where we show common effects of specific covariates across the trials.

Original languageEnglish (US)
Pages (from-to)1750-1766
Number of pages17
JournalStatistics in Medicine
Volume33
Issue number10
DOIs
StatePublished - May 10 2014

Fingerprint

Multi-state Model
Recurrence
Colonic Neoplasms
Cancer
Covariates
Randomized Trial
Bayesian Estimation
Clinical Trials
Markov Model
Neoplasms
Model
Survival
Estimate
Population

Keywords

  • Colon cancer
  • Cox-Snell residuals
  • Cure model
  • Deviance residuals
  • Multi-state model

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Medicine(all)

Cite this

Multi-state models for colon cancer recurrence and death with a cured fraction. / Conlon, A. S C; Taylor, J. M G; Sargent, D. J.

In: Statistics in Medicine, Vol. 33, No. 10, 10.05.2014, p. 1750-1766.

Research output: Contribution to journalArticle

Conlon, A. S C ; Taylor, J. M G ; Sargent, D. J. / Multi-state models for colon cancer recurrence and death with a cured fraction. In: Statistics in Medicine. 2014 ; Vol. 33, No. 10. pp. 1750-1766.
@article{9d644574b53e4800a45deccb913c05e0,
title = "Multi-state models for colon cancer recurrence and death with a cured fraction",
abstract = "In cancer clinical trials, patients often experience a recurrence of disease prior to the outcome of interest, overall survival. Additionally, for many cancers, there is a cured fraction of the population who will never experience a recurrence. There is often interest in how different covariates affect the probability of being cured of disease and the time to recurrence, time to death, and time to death after recurrence. We propose a multi-state Markov model with an incorporated cured fraction to jointly model recurrence and death in colon cancer. A Bayesian estimation strategy is used to obtain parameter estimates. The model can be used to assess how individual covariates affect the probability of being cured and each of the transition rates. Checks for the adequacy of the model fit and for the functional forms of covariates are explored. The methods are applied to data from 12 randomized trials in colon cancer, where we show common effects of specific covariates across the trials.",
keywords = "Colon cancer, Cox-Snell residuals, Cure model, Deviance residuals, Multi-state model",
author = "Conlon, {A. S C} and Taylor, {J. M G} and Sargent, {D. J.}",
year = "2014",
month = "5",
day = "10",
doi = "10.1002/sim.6056",
language = "English (US)",
volume = "33",
pages = "1750--1766",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "10",

}

TY - JOUR

T1 - Multi-state models for colon cancer recurrence and death with a cured fraction

AU - Conlon, A. S C

AU - Taylor, J. M G

AU - Sargent, D. J.

PY - 2014/5/10

Y1 - 2014/5/10

N2 - In cancer clinical trials, patients often experience a recurrence of disease prior to the outcome of interest, overall survival. Additionally, for many cancers, there is a cured fraction of the population who will never experience a recurrence. There is often interest in how different covariates affect the probability of being cured of disease and the time to recurrence, time to death, and time to death after recurrence. We propose a multi-state Markov model with an incorporated cured fraction to jointly model recurrence and death in colon cancer. A Bayesian estimation strategy is used to obtain parameter estimates. The model can be used to assess how individual covariates affect the probability of being cured and each of the transition rates. Checks for the adequacy of the model fit and for the functional forms of covariates are explored. The methods are applied to data from 12 randomized trials in colon cancer, where we show common effects of specific covariates across the trials.

AB - In cancer clinical trials, patients often experience a recurrence of disease prior to the outcome of interest, overall survival. Additionally, for many cancers, there is a cured fraction of the population who will never experience a recurrence. There is often interest in how different covariates affect the probability of being cured of disease and the time to recurrence, time to death, and time to death after recurrence. We propose a multi-state Markov model with an incorporated cured fraction to jointly model recurrence and death in colon cancer. A Bayesian estimation strategy is used to obtain parameter estimates. The model can be used to assess how individual covariates affect the probability of being cured and each of the transition rates. Checks for the adequacy of the model fit and for the functional forms of covariates are explored. The methods are applied to data from 12 randomized trials in colon cancer, where we show common effects of specific covariates across the trials.

KW - Colon cancer

KW - Cox-Snell residuals

KW - Cure model

KW - Deviance residuals

KW - Multi-state model

UR - http://www.scopus.com/inward/record.url?scp=84898011636&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84898011636&partnerID=8YFLogxK

U2 - 10.1002/sim.6056

DO - 10.1002/sim.6056

M3 - Article

C2 - 24307330

AN - SCOPUS:84898011636

VL - 33

SP - 1750

EP - 1766

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 10

ER -