Bayesian inference for smoking cessation with a latent cure state

Biometrics

Volumn 65, Issue 3, 2009, Pages 970-978

  Luo, Sheng ^a   Crainiceanu, Ciprian M ^b   Louis, Thomas A ^b   Chatterjee, Nilanjan ^c  

^a UNIVERSITY OF TEXAS SCHOOL OF PUBLIC HEALTH   (United States)

^b JOHNS HOPKINS UNIVERSITY   (United States)

^c NATIONAL CANCER INSTITUTE   (United States)

Author keywords

Cure model; MCMC; Mixed effects model; Prediction; Recurrent events; Smoking cessation

Indexed keywords

BAYESIAN NETWORKS; CURING; INFERENCE ENGINES; INTELLIGENT SYSTEMS; MARKOV PROCESSES; NORMAL DISTRIBUTION; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

BAYESIAN APPROACHES; BAYESIAN INFERENCE; CURE MODELS; CURE STATE; MCMC; MIXED EFFECTS MODELS; MODEL DYNAMICS; RECURRENT EVENTS; SMOKING CESSATIONS; SUBJECT-SPECIFIC;

MONTE CARLO METHODS;

ALPHA TOCOPHEROL; BETA CAROTENE;

BAYESIAN ANALYSIS; MARKOV CHAIN; MONTE CARLO ANALYSIS; PREDICTION; SMOKING; STOCHASTICITY;

ARTICLE; BAYES THEOREM; CANCER PREVENTION; CIGARETTE SMOKING; DECISION MAKING; DISEASE MODEL; DISEASE SIMULATION; LUNG CANCER; MATHEMATICAL ANALYSIS; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; NORMAL DISTRIBUTION; PREDICTION; PROBABILITY; SMOKING CESSATION; STOCHASTIC MODEL; TOBACCO DEPENDENCE;

ALGORITHMS; BAYES THEOREM; BIOMETRY; CLINICAL TRIALS AS TOPIC; COMPUTER SIMULATION; HUMANS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; OUTCOME ASSESSMENT (HEALTH CARE); PATIENT SELECTION; REACTION TIME; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SMOKING; SMOKING CESSATION;

EID: 70349246870     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2008.01167.x     Document Type: Article

References (21)

1. Auranen, K. (2000). Back-calculating the age-specific incidence of recurrent subclinical Haemophilus Influenzae type B infection. Statistics in Medicine 19, 281 296.
2. Auranen, K., Arjas, E., Leino, T., and Takala, A. K. (2000). Transmission of pneumococcal carriage in families: A latent Markov process model for binary longitudinal data. Journal of the American Statistical Association 95, 1044 1053.
3. CDC. (1997). Smoking-attributable mortality and years of potential life lost - United States, 1984. MMWR 46, 444 451.
4. Cowling, B. J., Hutton, J. L., and Shaw, J. E. H. (2006). Joint modelling of event counts and survival times. Journal of the Royal Statistical Society, Series C: Applied Statistics 55, 31 39.
5. Cui, Y., Wen, W., Moriarty, C. J., and Levine, R. S. (2006). Risk factors and their effects on the dynamic process of smoking relapse among veteran smokers. Behaviour Research and Therapy 44, 967 981.
6. Eerola, M., Gasbarra, D., Helena M. P., Linden, H., and Andreev, A. (2003). Joint modelling of recurrent infections and antibody response by Bayesian data augmentation. Scandinavian Journal of Statistics 30, 677 698.
7. Gelfand, A. E. and Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398 409.
8. Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721 741.
9. Glasgow, R. E. and Lichtenstein, E. (1987). Long-term effects of behavioral smoking cessation interventions. Behavior Therapy 18, 297 324.
10. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97 109.
11. Huang, X., Cormier, J. N., and Pisters, P. W. T. (2006). Estimation of the causal effects on survival of two-stage nonrandomized treatment sequences for recurrent diseases. Biometrics 62, 901 909.
12. Hunt, W. A., Barnett, L. W., and Brauch, L. G. (1971). Relapse rates in addiction programs. Journal of Clinical Psychology 27, 455 456.
13. Kalbfleisch, J. D. and Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data. Hoboken, New Jersey: John Wiley & Sons.
14. Li, C.-S., Taylor, J. M. G., and Sy, J. P. (2001). Identifiability of cure models. Statistics and Probability Letters 54, 389 395.
15. Luo, S., Crainiceanu, C. M., Louis, T., and Chatterjee, N. (2008). Analysis of smoking cessation patterns using a stochastic mixed effects model with a latent cured state. Journal of the American Statistical Association 103, 1002 1013.
16. McBride, P. E. (1992). The health consequences of smoking, cardiovascular diseases. The Medical Clinics of North America 76, 333 353.
17. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). Equations of state calculations by fast computing machine. Journal of Chemical Physics 21, 1087 1091.
18. Moller, J. and Syversveen, A. R. (1998). Log Gaussian Cox processes. Scandinavian Journal of Statistics 25, 451 482.
19. Novotny, T. E., Romano, R. A., Davis, R. M., and Mills, S. L. (1992). The public health practice of tobacco control: Lessons learned and directions for the states in the 1990s. Annual Review of Public Health 13, 287 318.
20. Piasecki, T. M., Fiore, M. C., McCarthy, D. E., and Baker, T. B. (2002). Have we lost our way The need for dynamic formulations of smoking relapse proneness. Addiction 97, 1093 1108.
21. Samet, J. M. (1992). The health benefits of smoking cessation. The Medical Clinics of North America 76, 399 414.