A multivariate finite mixture latent trajectory model with application to dementia studies

Journal of Applied Statistics

Volumn 43, Issue 14, 2016, Pages 2503-2523

  Lai, Dongbing ^a   Xu, Huiping ^b   Koller, Daniel ^a   Foroud, Tatiana ^a   Gao, Sujuan ^b  

^a INDIANA UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b INDIANA UNIVERSITY   (United States)

Author keywords

cognitive decline; dementia; Multivariate finite mixture latent trajectory

Indexed keywords

EID: 84959054034     PISSN: 02664763     EISSN: 13600532     Source Type: Journal    
DOI: 10.1080/02664763.2016.1141181     Document Type: Article

References (44)

1. N.S.Altman, An introduction to kernel and nearest-neighbor nonparametric regression, Amer. Statist. 46 (1992), pp. 175–185.
2. Alzheimer's Disease Genetics Consortium. Available from: https://alois.med.upenn.edu/adgc/about/overview.html
3. E.H.Corder, A.M.Saunders, W.J.Strittmatter, D.E.Schmechel, P.C.Gaskell, G.W.Small, A.D.Roses, J.L.Haines, and M.A.Pericak-Vance, Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families, Science 261 (1993), pp. 921–923. doi:10.1126/science.8346443
4. A.P.Dempster, N.M.Laird, and D.B.Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, J. R. Statist. Soc, Ser. B. 39 (1977), pp. 1–38.
5. M.A.Figueiredo, Lecture notes on Bayesian estimation and classification. 2004.
6. J.P.Fox, Bayesian item response modeling:Theory and applications, Springer, New York, 2010.
7. C.J.Galton, K.Patterson, J.H.Xuereb, and J.R.Hodges, Atypical and typical presentations of Alzheimer's disease:A clinical, neuropsychological, neuroimaging and pathological study of 13 cases, Brain 123 (Pt 3) (2000), pp. 484–498. doi:10.1093/brain/123.3.484
8. L.A.Goodman, On the assignment of individuals to latent classes, Sociol. Methodol. 37 (2007), pp. 1–22. doi:10.1111/j.1467-9531.2007.00184.x
9. S.M.Gray and R.Brookmeyer, Estimating a treatment effect from multidimensional longitudinal data, Biometrics 54 (1998), pp. 976–988. doi:10.2307/2533850
10. K.M.Hayden, B.R.Reed, J.J.Manly, D.Tommet, R.H.Pietrzak, G.J.Chelune, F.M.Yang, A.J.Revell, D.A.Bennett, and R.N.Jones, Cognitive decline in the elderly:An analysis of population heterogeneity, Age. Ageing 40 (2011), pp. 684–689. doi:10.1093/ageing/afr101
11. A.van den Hout, J.P.Fox, and G.Muniz-Terrera, Longitudinal mixed-effects models for latent cognitive function, Statist Model. 15 (2015), pp. 366–387. doi:10.1177/1471082X14555607
12. H.Jacqmin-Gadda, C.Proust-Lima, and H.Amieva, Semi-parametric latent process model for longitudinal ordinal data:Application to cognitive decline, Stat. Med. 29 (2010), pp. 2723–2731. doi:10.1002/sim.4035
13. R.H.Klein Entink, J.P.Fox, and A.van den Hout, A mixture model for the joint analysis of latent developmental trajectories and survival, Stat. Med. 30 (2011), pp. 2310–2325. doi:10.1002/sim.4266
14. A.Martin, P.Brouwers, F.Lalonde, C.Cox, P.Teleska, P.Fedio, N.L.Foster, and T.N.Chase, Towards a behavioral typology of Alzheimer's patients, J. Clin. Exp. Neuropsychol. 8 (1986), pp. 594–610. doi:10.1080/01688638608405178
15. G.McKhann, D.Drachman, M.Folstein, R.Katzman, D.Price, and E.M.Stadlan, Clinical diagnosis of Alzheimer's disease:Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease, Neurology 34 (1984), pp. 939–944. doi:10.1212/WNL.34.7.939
16. G.M.McKhann, D.S.Knopman, H.Chertkow, B.T.Hyman, C.R.Jack, C.H.Kawas, W.E.Klunk, W.J.Koroshetz, J.J.Manly, R.Mayeux, R.C.Mohs, J.C.Morris, M.N.Rossor, P.Scheltens, M.C.Carrillo, B.Thies, S.Weintraub, and C.H.Phelps, The diagnosis of dementia due to Alzheimer's disease:recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease, Alzheimers. Dement. 7 (2011), pp. 263–269. doi:10.1016/j.jalz.2011.03.005
17. G.J.McLachlan and T.Krishnan, The EM Algorithm and Extensions, John Wiley & Sons, Inc., New York, 1997.
18. G.J.McLachlan and D.Peel, Finite Mixture Models, Wiley-Intersci, New York, 2004.
19. B.L.Miller, J.L.Cummings, J.Villanueva-Meyer, K.Boone, C.M.Mehringer, I.M.Lesser, and I.Mena, Frontal lobe degeneration:clinical, neuropsychological, and SPECT characteristics, Neurology 41 (1991), pp. 1374–1382. doi:10.1212/WNL.41.9.1374
20. J.C.Morris, S.Weintraub, H.C.Chui, J.Cummings, C.Decarli, S.Ferris, N.L.Foster, D.Galasko, N.Graff-Radford, E.R.Peskind, D.Beekly, E.M.Ramos, and W.A.Kukull, The Uniform Data Set (UDS):Clinical and cognitive variables and descriptive data from Alzheimer Disease Centers, Alzheimer. Dis. Assoc. Disord. 20 (2006), pp. 210–216. doi:10.1097/01.wad.0000213865.09806.92
21. B.Muthen, Beyond SEM:General latent variable modeling, Behaviormetrika 29 (2002), pp. 81–117. doi:10.2333/bhmk.29.81
22. B.Muthen, Latent variable analysis:Growth mixture modeling and related techniques for longitudinal data, in The Sage Handbook of Quantitative Methodology for the Social Sciences, D.Kaplan, eds., Newbury Park, CA, 2004, pp. 345–368.
23. B.Muthen and K.Shedden, Finite mixture modeling with mixture outcomes using the EM algorithm, Biometrics 55 (1999), pp. 463–469. doi:10.1111/j.0006-341X.1999.00463.x
24. B.Muthen, C.H.Brown, K.Masyn, B.Jo, S.T.Khoo, C.C.Yang, C.P.Wang, S.G.Kellam, J.B.Carlin, and J.Liao, General growth mixture modeling for randomized preventive interventions, Biostatistics 3 (2002), pp. 459–475. doi:10.1093/biostatistics/3.4.459
25. D.S.Nagin, Analyzing developmental trajectories:A semiparametric, group-based approach, Psychol. Methods. 4 (1999), pp. 139–157. doi:10.1037/1082-989X.4.2.139
26. D.S.Nagin, Group-based Modeling of Development, Harvard University Press, Cambridge, MA, 2005. doi:10.4159/9780674041318
27. D.S.Nagin and C.L.Odgers, Group-based trajectory modeling in clinical research, Annu. Rev. Clin. Psychol. 6 (2010), pp. 109–138. doi:10.1146/annurev.clinpsy.121208.131413
28. D.Neary, J.S.Snowden, D.M.Bowen, N.R.Sims, D.M.Mann, J.S.Benton, B.Northen, P.O.Yates, and A.N.Davison, Neuropsychological syndromes in presenile dementia due to cerebral atrophy, J. Neurol. Neurosurg. Psychiatry 49(2) (1986), pp. 163–174. doi:10.1136/jnnp.49.2.163
29. D.Neary, J.S.Snowden, B.Northen, and P.Goulding, Dementia of frontal lobe type, J. Neurol. Neurosurg. Psychiatry 51 (1988), pp. 353–361. doi:10.1136/jnnp.51.3.353
30. D.Neary, J.S.Snowden, L.Gustafson, U.Passant, D.Stuss, S.Black, M.Freedman, A.Kertesz, P.H.Robert, M.Albert, K.Boone, B.L.Miller, J.Cummings, and D.F.Benson, Frontotemporal lobar degeneration:A consensus on clinical diagnostic criteria. Neurology 51 (1998), pp. 1546–1554. doi:10.1212/WNL.51.6.1546
31. D.Neary, J.Snowden, and D.Mann, Frontotemporal dementia. Lancet. Neuro. 4 (2005), pp. 771–780. doi:10.1016/S1474-4422(05)70223-4
32. S.K.Ng, G.J.McLachlan, K.Wang, L.Ben-Tovim Jones, and S.W.Ng, A mixture model with random-effects components for clustering correlated gene-expression profiles, Bioinformatics 22 (2006), pp. 1745–1752. doi:10.1093/bioinformatics/btl165
33. K.L.Nylund, T.Asparouhov, and B.O.Muthen, Deciding on the number of classes in latent class analysis and growth mixture modeling:A Monte Carlo simulation study, Struct. Equ. Model. 14 (2007), pp. 535–569. doi:10.1080/10705510701575396
34. R.J.Perry and J.R.Hodges, Differentiating frontal and temporal variant frontotemporal dementia from Alzheimer's disease, Neurology 54 (2000), pp. 2277–2284. doi:10.1212/WNL.54.12.2277
35. C.Proust, H.Jacqmin-Gadda, J.M.Taylor, J.Ganiayre, and D.Commenges, A nonlinear model with latent process for cognitive evolution using multivariate longitudinal data, Biometrics 62 (2006), pp. 1014–1024. doi:10.1111/j.1541-0420.2006.00573.x
36. C.Proust-Lima, L.Letenneur, and H.Jacqmin-Gadda, A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome, Stat. Med. 26 (2007), pp. 2229–2245. doi:10.1002/sim.2659
37. D.P.Salmon and M.W.Bondi, Neuropsychological assessment of dementia, Annu. Rev. Psychol. 60 (2009), pp. 257–282. doi:10.1146/annurev.psych.57.102904.190024
38. G.Schwartz, Estimating the dimension of a model, Ann. Stat. 6 (1978), pp. 461–464. doi:10.1214/aos/1176344136
39. W.J.Strittmatter, A.M.Saunders, D.Schmechel, M.Pericak-Vance, J.Enghild, G.S.Salvesen, and A.D.Roses, Apolipoprotein E:High-avidity binding to beta-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease, Proc. Natl. Acad. Sci. USA. 90 (1993), pp. 1977–1981. doi:10.1073/pnas.90.5.1977
40. The AlzGene database. Available from: http://www.alzgene.org/
41. The National Alzheimer's Coordinating Center. Available from: https://www.alz.washington.edu/
42. D.Tofighi and C.K.Enders, Identifying the correct number of classes in growth mixture models, Adv Latent Var Mixture Models. (2008) pp. 317–341.
43. S.Weintraub, D.Salmon, N.Mercaldo, S.Ferris, N.R.Graff-Radford, H.Chui, J.Cummings, C.DeCarli, N.L.Foster, D.Galasko, E.Peskind, W.Dietrich, D.L.Beekly, W.A.Kukull, and J.C.Morris, The Alzheimer's disease centers’ uniform data set (UDS):the neuropsychologic test battery, Alzheimer. Dis. Assoc. Disord. 23 (2009), pp. 91–101. doi:10.1097/WAD.0b013e318191c7dd
44. C.-C.Yang, Evaluating latent class analysis models in qualitative phenotype identification, Comput. Stat Data Anal. 50 (2006), pp. 1090–1104. doi:10.1016/j.csda.2004.11.004