A Longitudinal, Observational Study with Many Repeated Measures Demonstrated Improved Precision of Individual Survival Curves Using Bayesian Joint Modeling of Disability and Survival

REFERENCES

• Aalen, O. O. (2012). Armitage Lecture 2010: Understanding treatment effects: The value of integrating longitudinal data and survival analysis. Statistics in Medicine, 31, 1903–1917.
   PubMed Web of Science ®Google Scholar
• Aikake, H. (1973). Information theory and an extension of the maximum likelihood principle. Paper presented at the 2nd International Symposium on Information Theory, Budapest.
   Google Scholar
• Albert, P. S., & Follmann, D. A. (2000). Modeling repeated count data subject to informative dropout. Biometrics, 56, 667–677
   PubMed Web of Science ®Google Scholar
• Allison, P. D. (1982). Discrete-time methods for the analysis of event histories. Sociological Methods and Research, 15, 61–98.
   Google Scholar
• American Fact Finder. (2003). U.S. Census Bureau. Retrieved May 29, 2003, from http://factfinder.census.gov
   Google Scholar
• Brooks, S. P., & Gelman, A. (1998). Alternative methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7, 434–455.
   Web of Science ®Google Scholar
• Cowling, B. J., Hutton, J. L., & Shaw, J. E. H. (2006). Joint modelling of event counts and survival times. Journal of the Royal Statistical Society Series C, 55, 31–39.
   Web of Science ®Google Scholar
• Cox, D. R., & Oakes, D. (1984). Distributions of failure time. In D. R. Cox and D. V. Hinkley (Eds.), Analysis of survival data, CRC Monographs on Statistics & Applied Probability (Book 21) (p. 21). London: Chapman & Hall.
   Google Scholar
• Faucett, C., Schenker, N., & Elashoff, R. M. (1998). Analysis of censored survival data with intermittently observed time-dependent binary covariates. Journal of the American Statistical Association, 93, 427–437.
   Web of Science ®Google Scholar
• Gelman, A., & Hill, J. (2007). Simulation for checking statistical procedures and model fits. In Data analysis using regression and multilevel/hierarchical models (pp. 155–166). New York, NY: Cambridge University Press.
   Google Scholar
• Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences (with discussion). Statistical Science, 7, 457–511.
   Google Scholar
• Gill, T. M., Allore, H. G., Gahbauer, E. A., & Murphy, T. E. (2010). Change in disability after hospitalization or restricted activity in older persons. Journal of the American Medical Association, 304, 1919–1928.
   PubMed Web of Science ®Google Scholar
• Gill, T. M., Desai, M. M., Gahbauer, E. A., Holford, T. R., & Williams, C. S. (2001). Restricted activity among community-living older persons: Incidence, precipitants and health care utilization (with editorial). Annals of Internal Medicine, 135, 313–321.
   PubMed Web of Science ®Google Scholar
• Gill, T. M., Gahbauer, E. A., Han, L., & Allore, H. G. (2010). Trajectories of disability in the last year of life. New England Journal of Medicine, 362, 1173–1180.
   PubMed Web of Science ®Google Scholar
• Gill, T. M., Gahbauer, E. A., Allore, H. G., & Han, L. (2006). Transitions between frailty states among community-living older persons. Archives of Internal Medicine, 166, 418–423.
   PubMed Web of Science ®Google Scholar
• Guo, X., & Carlin, B. P. (2004). Separate and joint modeling of longitudinal and event time data using standard computer packages. The American Statistician, 58, 16–24. doi:10.1198/0003130042854
   Web of Science ®Google Scholar
• Hardy, S. E., & Gill, T. M. (2004). Recovery from disability among community-dwelling older persons. Journal of the American Medical Association, 291, 1596–1602.
   PubMed Web of Science ®Google Scholar
• Henderson, R. J., Diggle, P. J., & Dobson, A. (2000). Joint modelling of longitudinal measurements and event time data. Biostatistics, 1, 465–480.
   PubMedGoogle Scholar
• Hu, W., Mengersen, K., & Tong, S. (2009). Spatial analysis of notified cryptosporidiosis infections in Brisbane, Australia. Annals of Epidemiology, 19, 900–907.
   PubMed Web of Science ®Google Scholar
• Ibrahim, J., Chu, H., & Chen, L. M. (2010). Basic concepts and methods for joint models of longitudinal and survival data. Journal of Clinical Oncology, 28, 2796–2801.
   PubMed Web of Science ®Google Scholar
• Lin, I.-F., Chang, W. P., & Liao, Y.-N. (2013). Shrinkage methods enhanced the accuracy of parameter estimation using Cox models with small number of events. Journal of Clinical Epidemiology, 66, 743–751.
   PubMed Web of Science ®Google Scholar
• Murphy, T. E., Han, L., Allore, H. G., Peduzzi, P. N., Gill, T. M., & Lin, H. (2011). Treatment of death in the analysis of longitudinal studies of gerontological outcomes. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 66A, 109–114. doi:doi:10.1093/geronaglq188
   Google Scholar
• Ning, Y., McAvay, G., Chaudhry, S. I., Arnold, A., & Allore, H. G. (2013). Results differ by appying distinctive multiple imputation approaches on the longitudinal Cardiovascular Health Study data. Experimental Aging Research, 39, 27–43. doi:10.1080/0361073X.2013.741968.
   PubMed Web of Science ®Google Scholar
• Prentice, R. L., Kalbfleisch, J. D., Peterson, A. V., Flournoy, N., Farewell, V. T., & Breslow, N. E. (1978). The analysis of failure times in the presence of competing risks. Biometrics, 34, 541–554.
   PubMed Web of Science ®Google Scholar
• Rizopoulos D., Verbeke, G., Lesaffre, E., & Vanrenterghem, Y. (2008). A two-part joint model for the analysis of survival and longitudinal binary data with excess zeros. Biometrics, 64, 611–619.
   PubMed Web of Science ®Google Scholar
• Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.
   Web of Science ®Google Scholar
• Spiegelhalter, D. J., Best, N. J., Carlin, B. P., & van der Linde A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society B, 64, 583–640.
   Google Scholar
• Symons, J. M., Le, H. Q., Kreckman, K. H., Sakr, C. J., & Lednar, W. M. (2009). A Bayesian approach to occupational mortality surveillance. Annals of Epidemiology, 19, 676.
   Google Scholar
• Tsiatis, A. A., Degruttola, V., & Wulfsohn, M. S. (1995). Modeling the relationship of survival to longitudinal data measured with error, applications to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association, 90, 27–37.
   Web of Science ®Google Scholar
• Wulfsohn, M. S., & Tsiatis, A. A. (1997). A joint model for survival and longitudinal data measured with error. Biometrics, 53, 330–339.
   PubMed Web of Science ®Google Scholar
• Ye, W., Lin, X., & Taylor, J. M. G. (2008). Semiparametric modeling of longitudinal measurements and time-to-event data—A two-stage regression calibration approach. Biometrics, 64, 1238–1246.
   PubMed Web of Science ®Google Scholar