Joint modeling of multivariate longitudinal measurements and survival data with applications to Parkinson's disease

Statistical Methods in Medical Research

Volumn 25, Issue 4, 2016, Pages 1346-1358

  He, Bo ^a   Luo, Sheng ^a   Lawson, Andrew B ^a   Lee, Duncan ^a   MacNab, Ying ^a  

^a UNIVERSITY OF TEXAS   (United States)

Author keywords

item response theory; joint model; latent variable; Markov Chain Monte Carlo; mixed model

Indexed keywords

PLACEBO; SELEGILINE; TOCOPHEROL;

BAYES THEOREM; CLINICAL TRIAL (TOPIC); CONFERENCE PAPER; FACTORIAL DESIGN; HUMAN; MARKOV CHAIN; MATHEMATICAL COMPUTING; MONOTHERAPY; MONTE CARLO METHOD; MULTICENTER STUDY (TOPIC); MULTILEVEL ITEM RESPONSE THEORY MODEL; PARKINSON DISEASE; PROPORTIONAL HAZARDS MODEL; SIMULATION; SURVIVAL RATE; LONGITUDINAL STUDY; MULTIVARIATE ANALYSIS;

BAYES THEOREM; HUMANS; LONGITUDINAL STUDIES; MARKOV CHAINS; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; PARKINSON DISEASE; TOCOPHEROLS;

EID: 84983748455     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280213480877     Document Type: Conference Paper

References (38)

1. Henderson R, Diggle P, Dobson A,. Joint modelling of measurements and event time data. Biostatitics 2000; 1: 465-480.
2. Tsiatis A, Davidian M,. Joint modelling of longitudinal and time-to-event data: an overview. Stat Sin 2004; 14: 809-834.
3. Wulfsohn M, Tsiatis A,. A joint model for survival and longitudinal data measured with error. Biometrics 1997; 53: 330-339.
4. Brown E, Ibrahim J, DeGruttola V,. A flexible B-Spline model for multiple longitudinal biomarkers and survival. Biometrics 2005; 61: 64-73.
5. Rizopoulos D, Ghosh P,. A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event. Stat Med 2011; 30: 1366-1380.
6. Brown E, Ibrahim J,. Bayesian approaches to joint cure-rate and longitudinal models with applications to cancer vaccine trials. Biometrics 2003; 59: 686-693.
7. Elashoff R, Li G, Li N,. An approach to joint analysis of longitudinal measurements and competing risks failure time data. Stat Med 2007; 26: 2813-2835.
8. O'Brien L, Fitzmaurice G,. Analysis of longitudinal multiple-source binary data using generalized estimating equations. J R Stat Soc Ser C Appl Stat 2004; 53: 177-193.
9. Wang C, Douglas J, Anderson S,. Item response models for joint analysis of quality of life and survival. Stat Med 2002; 21: 129-142.
10. Glas C, Geerlings H, Van de laar M, et al. Analysis of longitudinal randomized clinical trials using item response models. Contemp Clin Trials 2008; 30: 158-170.
11. Huang L, Wang W,. The generalized multilevel facets model for longitudinal data. J Educ Behav Stat 2012; 37: 231-255.
12. Wang W, Liu C,. Formulation and application of the generalized multilevel facets model. Educ Psychol Meas 2007; 67: 583-605.
13. Bacci S, Caviezel V,. Multilevel IRT models for the university teaching evaluation. J Appl Stat 2011; 38: 2775-2791.
14. Douglas J,. Item response models for longitudinal quality of life data in clinical trials. Stat Med 1999; 18: 2917-2931.
15. Andrade D, Tavares H,. Item response theory for longitudinal data: population parameter estimation. J Multivariate Anal 2005; 95: 1-22.
16. Maier K,. A Rasch hierarchical measurement model. J Educ Behav Stat 2001; 26: 307-330.
17. Kamata A,. Item analysis by the hierarchical generalized linear model. J Educ Meas 2001; 38: 79-93.
18. Mislevy R,. Estimation of latent group effects. J Am Stat Assoc 1985; 80: 993-997.
19. Fox J,. Bayesian item response modeling: theory and applications, New York, USA: Springer-Verlag, 2010.
20. Skrondal A, Rabe-Hesketh S,. Generalized latent variable modeling: multilevel, longitudinal, and structural equation models Boca Raton, FL: CRC Press, 2004.
21. Skrondal A, Rabe-Hesketh S,. Latent variable modelling: a survey. Scand J Stat 2007; 34: 712-745.
22. Lord F, Novick M, Birnbaum A,. Statistical theories of mental test scores Boston, MA: Addison-Wesley, 1968.
23. Lawless J, Zhan M,. Analysis of interval-grouped recurrent-event data using piecewise constant rate functions. Canad J Stat 1998; 26: 549-565.
24. Feng S, Wolfe R, Port F,. Frailty survival model analysis of the national deceased donor kidney transplant dataset using Poisson variance structures. J Am Stat Assoc 2005; 100: 728-735.
25. Gelman A, Carlin J, Stern H, et al. Bayesian data analysis Boca Raton, FL: CRC Press, 2004.
26. Spiegelhalter D, Best N, Carlin B, et al. Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol 2002; 64: 583-639.
27. Kass R, Raftery A,. Bayes factors. J Am Stat Assoc 1995; 90: 773-795.
28. Lewis S, Raftery A,. Estimating Bayes factors via posterior simulation with the Laplace-Metropolis estimator. J Am Stat Assoc 1997; 92: 648-655.
29. Shoulson I,. DATATOP: a decade of neuroprotective inquiry. Parkinson Study Group. Deprenyl and Tocopherol Antioxidative Therapy of Parkinsonism. Ann Neurol 1998; 44: S160-S166.
30. Bushnell D, Martin M,. Quality of life and Parkinson's disease: translation and validation of the US Parkinson's disease questionnaire (PDQ-39). Qual Life Res 1999; 8: 345-350.
31. McRae C, Diem G, Vo A, et al. Schwab & England: standardization of administration. Movement Disord 2000; 15: 335-336.
32. Touloumi G, Babiker AG, Pocock SJ, et al. Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study. Stat Med 2001; 20: 3715-3728.
33. Lange K, Sinsheimer J,. Normal/independent distributions and their applications in robust regression. J Comput Graph Stat 1993; 2: 175-198.
34. Lachos V, Ghosh P, Arellano-Valle R,. Likelihood based inference for skew-normal independent linear mixed models. Stat Sin 2010; 20: 303-303.
35. Genton M, Loperfido N,. Generalized skew-elliptical distributions and their quadratic forms. Ann Inst Stat Math 2005; 57: 389-401.
36. Chiu T, Leonard T, Tsui K,. The matrix-logarithmic covariance model. J Am Stat Assoc 1996; 91: 198-210.
37. Davidian M, Giltinan D,. Nonlinear models for repeated measurement data 1995; Vol. 62. Boca Raton, FL: Chapman & Hall/CRC.
38. Pourahmadi M, Daniels M,. Dynamic conditionally linear mixed models for longitudinal data. Biometrics 2002; 58: 225-231.