2,428
Views
66
CrossRef citations to date
0
Altmetric
Applications and Case Studies

Combining Dynamic Predictions From Joint Models for Longitudinal and Time-to-Event Data Using Bayesian Model Averaging

Pages 1385-1397 | Received 01 Feb 2013, Published online: 22 Dec 2014

REFERENCES

  • Anderson, J.R., Cain, K.C., and Gelber, R.D. (1983), “Analysis of Survival by Tumor Response,” Journal of Clinical Oncology, 1, 710–719.
  • Antolini, L., Boracchi, P., and Biganzoli, E. (2005), “A Time-Dependent Discrimination Index for Survival Data,” Statistics in Medicine, 24, 3927–3944.
  • Bekkers, J.A., Klieverik, L.M., Raap, G.B., Takkenberg, J.J., and Bogers, A.J. (2011), “Re-Operations for Aortic Allograft Root Failure: Experience From a 21-Year Single-Center Prospective Follow-up Study,” European Journal of Cardio-Thoracic Surgery, 40, 35–42.
  • Brown, E.R. (2009), “Assessing the Association Between Trends in a Biomarker and Risk of Event With an Application in Pediatric HIV/AIDS,” The Annals of Applied Statistics, 3, 1163–1182.
  • Brown, E.R., Ibrahim, J.G., and DeGruttola, V. (2005), “A Flexible B-Spline Model for Multiple Longitudinal Biomarkers and Survival,” Biometrics, 61, 64–73.
  • Faucett, C.L., and Thomas, D.C. (1996), “Simultaneously Modelling Censored Survival Data and Repeatedly Measured Covariates: A Gibbs Sampling Approach,” Statistics in Medicine, 15, 1663–1685.
  • Fisher, L., and Lin, D.-Y. (1999), “Time-Dependent Covariates in the Cox Proportional-Hazards Regression Model,” Annual Review of Public Health, 20, 145–157.
  • Geyer, C.J. (2011), “Importance Sampling, Simulated Tempering, and Umbrella Sampling,” in Handbook of Markov Chain Monte Carlo, eds. S. Brooks, A. Gelman, G. Jones, and X.-L. Meng, Boca Raton, FL: Chapman & Hall/CRC Press.
  • Guo, X., and Carlin, B.P. (2004), “Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages,” The American Statistician, 58, 16–24.
  • Harrell, F.E. (2001), Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis, New York: Springer-Verlag.
  • Harrell, F.E., Kerry, L.L., and Mark, D.B. (1996), “Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors,” Statistics in Medicine, 15, 361–387.
  • Hatfield, L.A., Boye, M.E., Hackshaw, M.D., and Carlin, B.P. (2012), “Multilevel Bayesian Models for Survival Times and Longitudinal Patient-Reported Outcomes With Many Zeros,” Journal of the American Statistical Association, 107, 875–885.
  • Henderson, R., Diggle, P., and Dobson, A. (2000), “Joint Modelling of Longitudinal Measurements and Event Time Data,” Biostatistics, 1, 465–480.
  • (2002), “Identification and Efficacy of Longitudinal Markers for Survival,” Biostatistics, 3, 33–50.
  • Hoeting, J.A., Madigan, D., Raftery, A.A., and Volinsky, C.T. (1999), “Bayesian Model Averaging: A Tutorial,” Statistical Science, 14, 382–417.
  • Huang, X., Li, G., Elashoff, R., and Pan, J. (2011), “A General Joint Model for Longitudinal Measurements and Competing Risks Survival Data With Heterogeneous Random Effects,” Lifetime Data Analysis, 17, 80–100.
  • Huang, X., Stefanski, L.A., and Davidian, M. (2009), “Latent-Model Robustness in Joint Models for a Primary Endpoint and a Longitudinal Process,” Biometrics, 64, 719–727.
  • Ibrahim, J.G., Chen, M., and Sinha, D. (2001), Bayesian Survival Analysis, New York: Springer-Verlag.
  • Kuhn, M., and Johnson, K. (2013), Applied Predictive Modeling, New York: Springer-Verlag.
  • Liu, L., and Huang, X. (2009), “Joint Analysis of Correlated Repeated Measures and Recurrent Events Processes in the Presence of Death, With Application to a Study on Acquired Immune Deficiency Syndrome,” Journal of the Royal Statistical Society, Series C, 58, 65–81.
  • Proust-Lima, C., and Taylor, J.M. (2009), “Development and Validation of a Dynamic Prognostic Tool for Prostate Cancer Recurrence Using Repeated Measures of Posttreatment PSA: A Joint Modeling Approach,” Biostatistics, 10, 535–549.
  • Rizopoulos, D. (2011), “Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data,” Biometrics, 67, 819–829.
  • ——— (2012), Joint Models for Longitudinal and Time-to-Event Data, With Applications in R, Boca Raton, FL: Chapman & Hall/CRC.
  • Rizopoulos, D., and Ghosh, P. (2011), “A Bayesian Semiparametric Multivariate Joint Model for Multiple Longitudinal Outcomes and a Time-to-Event,” Statistics in Medicine, 30, 1366–1380.
  • Rizopoulos, D., Verbeke, G., and Molenberghs, G. (2008), “Shared Parameter Models Under Random Effects Misspecification,” Biometrika, 95, 63–74.
  • Royston, P., and Sauerbrei, W. (2008), Multivariable Model-Building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables, New York: Wiley.
  • Steyerberg, E.W. (2010), Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating, New York: Springer-Verlag.
  • Sylvestre, M.-P., and Abrahamowicz, M. (2009), “Flexible Modeling of the Cumulative Effects of Time-Dependent Exposures on the Hazard,” Statistics in Medicine, 28, 3437–3453.
  • Taylor, J. M.G., Park, Y., Ankerst, D.P., Proust-Lima, C., Williams, S., Kestin, L., Bae, K., Pickles, T., and Sandler, H. (2013), “Real-Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models,” Biometrics, 69, 206–213.
  • Tierney, L., and Kadane, J.B. (1986), “Accurate Approximations for Posterior Moments and Marginal Densities,” Journal of the American Statistical Association, 81, 82–86.
  • Tsiatis, A.A., and Davidian, M. (2004), “Joint Modeling of Longitudinal and Time-to-Event Data: An Overview,” Statistica Sinica, 14, 809–834.
  • van Houwelingen, H.C. (2007), “Dynamic Prediction by Landmarking in Event History Analysis,” Scandinavian Journal of Statistics, 34, 70–85.
  • van Houwelingen, H.C., and Putter, H. (2011), Dynamic Prediction in Clinical Survival Analysis, Boca Raton, FL: Chapman & Hall/CRC.
  • Wulfsohn, M.S., and Tsiatis, A.A. (1997), “A Joint Model for Survival and Longitudinal Data Measured With Error,” Biometrics, 53, 330–339.
  • Ye, W., Lin, X., and Taylor, J.M. (2008), “Semiparametric Modeling of Longitudinal Measurements and Time-to-Event Data—A Two Stage Regression Calibration Approach,” Biometrics, 64, 1238–1246.
  • Yu, M., Taylor, J. M.G., and Sandler, H. (2008), “Individualized Prediction in Prostate Cancer Studies Using a Joint Longitudinal-Survival-Cure Model,” Journal of the American Statistical Association, 103, 178–187.
  • Zheng, Y., and Heagerty, P.J. (2007), “Prospective Accuracy for Longitudinal Markers,” Biometrics, 63, 332–341.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.