Bayesian inference for smoking cessation with a latent cure state

Research output: Contribution to journalArticle

Abstract

We present a Bayesian approach to modeling dynamic smoking addiction behavior processes when cure is not directly observed due to censoring. Subject-specific probabilities model the stochastic transitions among three behavioral states: smoking, transient quitting, and permanent quitting (absorbent state). A multivariate normal distribution for random effects is used to account for the potential correlation among the subject-specific transition probabilities. Inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation. This framework provides various measures of subject-specific predictions, which are useful for policy-making, intervention development, and evaluation. Simulations are used to validate our Bayesian methodology and assess its frequentist properties. Our methods are motivated by, and applied to, the Alpha-Tocopherol, Beta-Carotene Lung Cancer Prevention study, a large (29,133 individuals) longitudinal cohort study of smokers from Finland.

Original languageEnglish (US)
Pages (from-to)970-978
Number of pages9
JournalBiometrics
Volume65
Issue number3
DOIs
StatePublished - Sep 2009

Fingerprint

Smoking
Smoking Cessation
Bayesian inference
Markov Chain Monte Carlo Simulation
Markov Chains
absorbents
Cohort Study
Bayes Theorem
Multivariate Normal Distribution
Lung Cancer
Policy Making
Normal Distribution
beta Carotene
Probability Model
alpha-Tocopherol
Normal distribution
Dynamic Modeling
lung neoplasms
Finland
Censoring

Keywords

  • Cure model
  • MCMC
  • Mixed-effects model
  • Prediction
  • Recurrent events
  • Smoking cessation

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Medicine(all)

Cite this

Bayesian inference for smoking cessation with a latent cure state. / Luo, Sheng; Crainiceanu, Ciprian M; Louis, Thomas; Chatterjee, Nilanjan.

In: Biometrics, Vol. 65, No. 3, 09.2009, p. 970-978.

Research output: Contribution to journalArticle

@article{7172990121ba460090d884e47e67350a,
title = "Bayesian inference for smoking cessation with a latent cure state",
abstract = "We present a Bayesian approach to modeling dynamic smoking addiction behavior processes when cure is not directly observed due to censoring. Subject-specific probabilities model the stochastic transitions among three behavioral states: smoking, transient quitting, and permanent quitting (absorbent state). A multivariate normal distribution for random effects is used to account for the potential correlation among the subject-specific transition probabilities. Inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation. This framework provides various measures of subject-specific predictions, which are useful for policy-making, intervention development, and evaluation. Simulations are used to validate our Bayesian methodology and assess its frequentist properties. Our methods are motivated by, and applied to, the Alpha-Tocopherol, Beta-Carotene Lung Cancer Prevention study, a large (29,133 individuals) longitudinal cohort study of smokers from Finland.",
keywords = "Cure model, MCMC, Mixed-effects model, Prediction, Recurrent events, Smoking cessation",
author = "Sheng Luo and Crainiceanu, {Ciprian M} and Thomas Louis and Nilanjan Chatterjee",
year = "2009",
month = "9",
doi = "10.1111/j.1541-0420.2008.01167.x",
language = "English (US)",
volume = "65",
pages = "970--978",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "3",

}

TY - JOUR

T1 - Bayesian inference for smoking cessation with a latent cure state

AU - Luo, Sheng

AU - Crainiceanu, Ciprian M

AU - Louis, Thomas

AU - Chatterjee, Nilanjan

PY - 2009/9

Y1 - 2009/9

N2 - We present a Bayesian approach to modeling dynamic smoking addiction behavior processes when cure is not directly observed due to censoring. Subject-specific probabilities model the stochastic transitions among three behavioral states: smoking, transient quitting, and permanent quitting (absorbent state). A multivariate normal distribution for random effects is used to account for the potential correlation among the subject-specific transition probabilities. Inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation. This framework provides various measures of subject-specific predictions, which are useful for policy-making, intervention development, and evaluation. Simulations are used to validate our Bayesian methodology and assess its frequentist properties. Our methods are motivated by, and applied to, the Alpha-Tocopherol, Beta-Carotene Lung Cancer Prevention study, a large (29,133 individuals) longitudinal cohort study of smokers from Finland.

AB - We present a Bayesian approach to modeling dynamic smoking addiction behavior processes when cure is not directly observed due to censoring. Subject-specific probabilities model the stochastic transitions among three behavioral states: smoking, transient quitting, and permanent quitting (absorbent state). A multivariate normal distribution for random effects is used to account for the potential correlation among the subject-specific transition probabilities. Inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation. This framework provides various measures of subject-specific predictions, which are useful for policy-making, intervention development, and evaluation. Simulations are used to validate our Bayesian methodology and assess its frequentist properties. Our methods are motivated by, and applied to, the Alpha-Tocopherol, Beta-Carotene Lung Cancer Prevention study, a large (29,133 individuals) longitudinal cohort study of smokers from Finland.

KW - Cure model

KW - MCMC

KW - Mixed-effects model

KW - Prediction

KW - Recurrent events

KW - Smoking cessation

UR - http://www.scopus.com/inward/record.url?scp=70349246870&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70349246870&partnerID=8YFLogxK

U2 - 10.1111/j.1541-0420.2008.01167.x

DO - 10.1111/j.1541-0420.2008.01167.x

M3 - Article

C2 - 19173701

AN - SCOPUS:70349246870

VL - 65

SP - 970

EP - 978

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 3

ER -