Joint Modeling and Clustering Paired Generalized Longitudinal Trajectories With Application to Cocaine Abuse Treatment Data

REFERENCES

• Anton, R.F., O’Malley, S.S., Ciraulo, D. A., Cisler, R. A., Couper, D., and Donovan, D. M. (2006), Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence The COMBINE Study: A Randomized Controlled Trial, Journal of the American Medical Association, 295, 2003–2017.
   PubMed Web of Science ®Google Scholar
• Banfield, J.D., Raftery, A.E. (1993), Model-Based Gaussian and Non-Gaussian Clustering, Biometrics, 49, 803–821.
   Web of Science ®Google Scholar
• Booth, J.G., Hobert, J.P. (1999), Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm, Journal of the Royal Statistical Society, 61, 265–285.
   Google Scholar
• Carroll, K.C., Power, M., Bryant, K., Rounsaville, B.J. (1993), One Year Follow-Up Status of Treatment-Seeking Cocaine Abusers: Psychopathology and Dependence Severity as Predictors Of Outcome, Journal of Nervous and Mental Disease, 181, 71–79.
   PubMed Web of Science ®Google Scholar
• Crainiceanu, C.M., Staicu, A-M, Di, C.Z. (2009), Generalized Multilevel Functional Regression, Journal of the American Statistical Association, 104, 1550–1561.
   PubMed Web of Science ®Google Scholar
• Chan, K.S., Ledolter, J. (1995), Monte Carlo EM Estimation for Time Series Models Involving Counts, Journal of the American Statistical Association, 90, 242–252.
   Web of Science ®Google Scholar
• Chiou, J.-M., Li, P.-L. (2007), Functional Clustering and Identifying Substructures of Longitudinal Data, Journal of the Royal Statistical Society, 69, 679–699.
   Google Scholar
• Chiou, J.-M., Li, P.-L. (2008), Correlation-Based Functional Clustering via Subspace Projection, Journal of The American Statistical Association, 103, 1684–1692.
   Web of Science ®Google Scholar
• Di, C.Z., Crainiceanu, C.M., Caffo, B.S., Punjabi, N.M. (2009), Multilevel Functional Principal Component Analysis, Annals of Applied Statistics, 3, 458–488.
   PubMed Web of Science ®Google Scholar
• Fals-Stewart, W., O’Farrell, T.J., Freitas, T.T., McFarlin, S.K., Rutigliano, P. (2000), The Timeline Follow-Back Reports of Psychoactive Substance Use by Drug-Abusing Patients: Psychometric Properties, Journal of Consulting and Clinical Psychology, 68, 134–144.
   PubMed Web of Science ®Google Scholar
• Fox, H.C., Garcia, M., Milivojevic, V., Kreek, M.J., Sinha, R. (2006), Gender Differences in Cardiovascular and Corticoadrenal Response to Stress and Drug Cues in Cocaine Dependent Individuals, Psychopharmacology, 185, 348–357.
   PubMed Web of Science ®Google Scholar
• Geman, S., Geman, D. (1984), Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, IEEE Transactions on Pattern Analysis and Machine Intellegence, 6, 721–741.
   PubMed Web of Science ®Google Scholar
• Guan, Y., Li, Y., Sinha, R. (2011), Cocaine Dependence Treatment Data: Methods for Measurement Error Problems With Predictors Derived From Stationary Stochastic Processes, Journal of the American Statistical Association, 106, 480–493.
   PubMed Web of Science ®Google Scholar
• James, G., Hastie, T., Sugar, C. (2000), Principal Component Models for Sparse Functional Data, Biometrika, 87, 587–602.
   Web of Science ®Google Scholar
• James, G., Sugar, C. (2003), Clustering for Sparsely Sampled Functional Data, Journal of the American Statistical Association, 98, 397–408.
   Web of Science ®Google Scholar
• Jiang, H., Serban, N. (2011), Clustering Random Curves Under Spatial Interdependence With Application to Service Accessibility, Technometrics, 54, 108–119.
   Web of Science ®Google Scholar
• Hall, P., Müller, H.G., Wang, J. -L. (2006), Properties of Principal Component Methods for Functional and Longitudinal Data Analysis, The Annals of Statistics, 34, 1493–1517.
   Web of Science ®Google Scholar
• Hall, P., Müller, H.G., Yao, F. (2008), Modelling Sparse Generalized Longitudinal Observations With Latent Gaussian Processes, Journal of the Royal Statistical Society, 70, 703–723.
   Google Scholar
• Hubert, L.J., Arabie, P. (1985), Comparing Partitions, Journal of Classification, 2, 193–218.
   Web of Science ®Google Scholar
• Ibrahim, J.G., Zhu, H., Tang, N. (2008), Model Selection Criteria for Missing-Data Problems Using the EM Algorithm, Journal of the American Statistical Association, 103, 1648–1658.
   PubMed Web of Science ®Google Scholar
• Li, T. (2006), A Unified View on Clustering Binary Data, Machine Learning, 62, 199–215.
   Web of Science ®Google Scholar
• Li, Y., Hsing, T. (2010), Uniform Convergence Rates for Nonparametric Regression and Principal Component Analysis in Functional/Longitudinal Data, The Annals of Statistics, 38, 3321–3351.
   Web of Science ®Google Scholar
• McCulloch, C.E. (1997), Maximum Likelihood Algorithms for Generalized Linear Mixed Models, Journal of the American Statistical Association, 92, 162–170.
   Web of Science ®Google Scholar
• McCulloch, P., and Nelder, J. (1989), Generalized Linear Model ( 2nd ed.), London: Chapman & Hall.
   Google Scholar
• Ordonez, C. (2003), Clustering Binary Data Streams With k-Means, Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, . , DMKD 2003, San Diego, CA,12–19.
   Google Scholar
• Ramsay, J.O., and Silverman, B.W. (2005), Functional Data Analysis ( 2nd ed.), New York: Springer-Verlag.
   Google Scholar
• Rand, W.M. (1971), Objective Criteria for the Evaluation of Clustering Methods, Journal of the American Statistical Association, 66, 846–850.
   Web of Science ®Google Scholar
• Rao, J. N.K., Scott, A.J. (1992), A Simple Method for the Analysis of Clustered Binary Data, Biometrics, 48, 577–585.
   PubMed Web of Science ®Google Scholar
• Serban, N., Jiang, H. (2012), Multilevel Functional Clustering Analysis, Biometrics, 68, 805–814.
   PubMed Web of Science ®Google Scholar
• Sinha, R., Garcia, M., Paliwal, P., Kreek, M.J., Rounsaville, B.J. (2006), Stress-Induced Cocaine Craving and Hypothalamic-Pituitary-Adrenal Responses are Predictive of Cocaine Relapse Outcomes, Archives of General Psychiatry, 633, 324–331.
   Web of Science ®Google Scholar
• Sobell, L., and Sobell, M. (1993), “Timeline Follow Back: A Technique for Assessing Self-Reported Ethanol Consumption,” in Techniques to Assess Alcohol Consumption, eds. J. Allen and R. Litten, Totowa, NJ: Humana Press, Inc, 41–42.
   Google Scholar
• Tanner, M.A. (1993), Tools for Statistical Inference: Observed Data and Data Augmentation ( 2nd ed.), Berlin: Springer-Verlag.
   Google Scholar
• Wei, C.G., Tanner, M.A. (1990), A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85, 699–704.
   Web of Science ®Google Scholar
• Yao, F., Müller, H.G., Wang, J.L. (2005a), Functional Data Analysis for Sparse Longitudinal Data, Journal of the American Statistical Association, 100, 577–590.
   Web of Science ®Google Scholar
• Yao, F., Müller, H.G., Wang, J.L. (2005b), Functional Linear Regression Analysis for Longitudinal Data, The Annals of Statistics, 33, 2873–2903.
   Web of Science ®Google Scholar
• Zhou, L., Huang, J.Z., Carroll, R.J. (2008), Joint Modelling of Paired Sparse Functional Data Using Principal Components, Biometrika, 95, 3, 601–619.
   PubMed Web of Science ®Google Scholar
• Zhou, L., Huang, J.Z., Martinez, J.G., Maity, A., Baladandayuthapani, V., Carroll, R.J. (2010), Reduced Rank Mixed Effects Models for Spatially Correlated Hierarchical Functional Data, Journal of the American Statistical Association, 105, 390–400.
   PubMed Web of Science ®Google Scholar