Mixture of Regressions with Multivariate Responses for Discovering Subtypes in Alzheimer’s Biomarkers with Detection Limits

References

• Amemiya T. 1973. Regression analysis when the dependent variable is truncated normal. Econometrica. 41(6):997.
   Web of Science ®Google Scholar
• Benaglia T, Chauveau D, Hunter DR, Young DS. 2009. mixtools: An R package for analyzing mixture models. J Stat Soft. 32(6):1–29.
   Web of Science ®Google Scholar
• Biernacki C, Celeux G, Govaert G. 2000. Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans Pattern Anal Machine Intell. 22(7):719–725.
   Web of Science ®Google Scholar
• Blennow K, Dubois B, Fagan AM, Lewczuk P, De Leon MJ, Hampel H. 2015. Clinical utility of cerebrospinal fluid biomarkers in the diagnosis of early Alzheimer’s disease. Alzheimers Dement. 11(1):58–69.
   PubMed Web of Science ®Google Scholar
• Caudill SB. 2012. A partially adaptive estimator for the censored regression model based on a mixture of normal distributions. Stat Methods Appl. 21(2):121–137.
   Web of Science ®Google Scholar
• Chételat G, Arbizu J, Barthel H, Garibotto V, Law I, Morbelli S, van de Giessen E, Agosta F, Barkhof F, Brooks DJ, et al. 2020. Amyloid-pet and 18f-fdg-pet in the diagnostic investigation of Alzheimer’s disease and other dementias. Lancet Neurol. 19(11):951–962.
   PubMed Web of Science ®Google Scholar
• Coates A, Ng AY. 2012. Learning feature representations with K-means. In: Montavon G, Orr GB, Müller KR, editors. Neural networks: tricks of the trade, chapter 22. 2nd ed. Berlin; Heidelberg: Springer. p. 561–580.
   Google Scholar
• Collins J, Huynh M. 2014. Estimation of diagnostic test accuracy without full verification: a review of latent class methods. Stat Med. 33(24):4141–4169.
   PubMed Web of Science ®Google Scholar
• De Alencar F, Galarza C, Matos L, Lachos V. 2020. CensMFM: finite mixture of multivariate censored/missing data. R Package Version 2. https://CRAN.R-project.org/package=CensMFM
   Google Scholar
• Dubois B, Feldman HH, Jacova C, Dekosky ST, Barberger-Gateau P, Cummings J, Delacourte A, Galasko D, Gauthier S, Jicha G, et al. 2007. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS–ADRDA criteria. Lancet Neurol. 6(8):734–746.
   PubMed Web of Science ®Google Scholar
• Fair RC. 1977. A note on the computation of the Tobit estimator. Econometrica. 45(7):1723.
   Web of Science ®Google Scholar
• Fithian W, Sun D, Taylor J. 2017. Optimal inference after model selection. arXiv preprint arXiv:1410.2597.
   Google Scholar
• Fleisher AS, Pontecorvo MJ, Devous MD, Lu M, Arora AK, Truocchio SP, Aldea P, Flitter M, Locascio T, Devine M, et al. 2020. Positron emission tomography imaging with [18f] flortaucipir and postmortem assessment of Alzheimer disease neuropathologic changes. JAMA Neurol. 77(7):829–839.
   PubMed Web of Science ®Google Scholar
• Galarza CE, Kan R, Lachos VH. 2021. MomTrunc: moments of folded and doubly truncated multivariate distributions. R Package Version 5.97. https://CRAN.R-project.org/package=MomTrunc
   Google Scholar
• Garay A, Lachos V, Massuia M. 2013. SMNCensReg: fitting univariate censored regression model under the scale mixture of normal distributions. R Package Version 2. https://CRAN.R-project.org/package=SMNCensReg
   Google Scholar
• Garay AM, Lachos VH, Bolfarine H, Cabral CR. 2017. Linear censored regression models with scale mixtures of normal distributions. Stat Papers. 58(1):247–278.
   Web of Science ®Google Scholar
• Garrett SL, McDaniel D, Obideen M, Trammell AR, Shaw LM, Goldstein FC, Hajjar I. 2019. Racial disparity in cerebrospinal fluid amyloid and tau biomarkers and associated cutoffs for mild cognitive impairment. JAMA Netw Open. 2(12):e1917363.
   PubMed Web of Science ®Google Scholar
• Goetz ME, Hanfelt JJ, John SE, Bergquist SH, Loring DW, Quyyumi A, Clifford GD, Vaccarino V, Goldstein F, Johnson TM, et al. 2019. Rationale and design of the Emory Healthy Aging and Emory Healthy Brain Studies. Neuroepidemiology. 53(3–4):187–200.
   PubMed Web of Science ®Google Scholar
• Goldfeld SM, Quandt RE. 1973. A Markov model for switching regressions. J Econom. 1(1):3–15.
   Google Scholar
• Hahsler M, Piekenbrock M, Doran D. 2019. dbscan: fast density-based clustering with R. J Stat Soft. 91(1):1–30.
   Web of Science ®Google Scholar
• Hampel H, Bürger K, Teipel SJ, Bokde AL, Zetterberg H, Blennow K. 2008. Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease. Alzheimers Dement. 4(1):38–48.
   PubMed Web of Science ®Google Scholar
• Hanson T, Johnson WO. 2002. Modeling regression error with a mixture of Polya trees. J Am Stat Assoc. 97(460):1020–1033.
   Web of Science ®Google Scholar
• Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE. 1991. Adaptive mixtures of local experts. Neural Comput. 3(1):79–87.
   PubMed Web of Science ®Google Scholar
• Jansen WJ, Janssen O, Tijms BM, Vos SJB, Ossenkoppele R, Visser PJ, Aarsland D, Alcolea D, Altomare D, von Arnim C, et al. 2022. Prevalence estimates of amyloid abnormality across the Alzheimer disease clinical spectrum. JAMA Neurol. 79(3):228–243.
   PubMed Web of Science ®Google Scholar
• Jansen WJ, Ossenkoppele R, Knol DL, Tijms BM, Scheltens P, Verhey FRJ, Visser PJ, Aalten P, Aarsland D, Alcolea D, et al. 2015. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA. 313(19):1924–1938.
   PubMed Web of Science ®Google Scholar
• Jedidi K, Ramaswamy V, Desarbo WS. 1993. A maximum likelihood method for latent class regression involving a censored dependent variable. Psychometrika. 58(3):375–394.
   Web of Science ®Google Scholar
• Karlsson M, Laitila T. 2014. Finite mixture modeling of censored regression models. Stat Papers. 55(3):627–642.
   Web of Science ®Google Scholar
• Khalili A, Lin S. 2013. Regularization in finite mixture of regression models with diverging number of parameters. Biometrics. 69(2):436–446.
   PubMed Web of Science ®Google Scholar
• Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, Bergström M, Savitcheva I, Huang G-F, Estrada S, et al. 2004. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh compound-b. Ann Neurol. 55(3):306–319.
   PubMed Web of Science ®Google Scholar
• Kolanko MA, Win Z, Loreto F, Patel N, Carswell C, Gontsarova A, Perry RJ, Malhotra PA. 2020. Amyloid pet imaging in clinical practice. Pract Neurol. 20(6):451–462.
   PubMedGoogle Scholar
• Lee G, Scott C. 2012. EM algorithms for multivariate Gaussian mixture models with truncated and censored data. Comput Stat Data Anal. 56(9):2816–2829.
   Web of Science ®Google Scholar
• Leisch F. 2004. FlexMix: a general framework for finite mixture models and latent class regression in R. J Stat Softw. 11(8):1–18.
   Google Scholar
• Linzer DA, Lewis JB. 2011. poLCA: an R package for polytomous variable latent class analysis. J Stat Soft. 42(10):1–29.
   Web of Science ®Google Scholar
• Maschke KJ, Gusmano MK. 2017. Medicare and amyloid pet imaging: the battle over evidence. J Aging Soc Policy. 29(2):105–122.
   PubMed Web of Science ®Google Scholar
• McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. 1984. 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(7):939–944.
   PubMed Web of Science ®Google Scholar
• McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr., Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, et al. 2011. 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(3):263–269.
   PubMed Web of Science ®Google Scholar
• McLachlan GJ, Peel D. 2000. Finite mixture models. New Jersey: Wiley.
   Google Scholar
• Meyer GD, Shapiro F, Vanderstichele H, Vanmechelen E, Engelborghs S, Deyn PPD, Coart E, Hansson O, Minthon L, Zetterberg H, et al. 2010. Diagnosis-independent Alzheimer disease biomarker signature in cognitively normal elderly people. Arch Neurol. 67(8):949–956.
   PubMedGoogle Scholar
• Morris JC, Roe CM, Xiong C, Fagan AM, Goate AM, Holtzman DM, Mintun MA. 2010. APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging. Ann Neurol. 67(1):122–131.
   PubMed Web of Science ®Google Scholar
• Morris JC, Schindler SE, McCue LM, Moulder KL, Benzinger TL, Cruchaga C, Fagan AM, Grant E, Gordon BA, Holtzman DM, et al. 2019. Assessment of racial disparities in biomarkers for Alzheimer disease. JAMA Neurol. 76(3):264–273.
   PubMed Web of Science ®Google Scholar
• O’Hagan A, Murphy TB, Scrucca L, Gormley IC. 2019. Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap. Comput Stat. 34(4):1779–1813.
   Web of Science ®Google Scholar
• Quandt RE, Ramsey JB. 1978. Estimating mixtures of normal distributions and switching regressions. J Am Stat Assoc. 73(364):730–738.
   Web of Science ®Google Scholar
• Ruud PA. 1991. Extensions of estimation methods using the EM algorithm. J Econom. 49(3):305–341.
   Web of Science ®Google Scholar
• Sanchez LB, Lachos VH, Moreno EJL, Sanchez MLB, LazyData T. 2018. Package ‘censmixreg’. R package version 3.1.
   Google Scholar
• Scrucca L, Fop M, Murphy TB, Raftery AE. 2016. mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. R J. 8(1):289–317.
   PubMedGoogle Scholar
• Seo SW, Ayakta N, Grinberg LT, Villeneuve S, Lehmann M, Reed B, DeCarli C, Miller BL, Rosen HJ, Boxer AL, et al. 2017. Regional correlations between [11c] pib pet and post-mortem burden of amyloid-beta pathology in a diverse neuropathological cohort. Neuroimage Clin. 13:130–137.
   PubMed Web of Science ®Google Scholar
• Shin J, Doraiswamy PM. 2016. Underrepresentation of African-Americans in Alzheimer’s trials: a call for affirmative action. Front Aging Neurosci. 8:123.
   PubMed Web of Science ®Google Scholar
• Vermunt JK, Magidson J. 2021. Lg-syntax user’s guide: manual for latent gold syntax module version 6.0. Arlington (MA): Statistical Innovations Inc.
   Google Scholar
• Wang WL, Castro LM, Hsieh WC, Lin TI. 2021. Mixtures of factor analyzers with covariates for modeling multiply censored dependent variables. Stat Papers. 62(5):2119–2145.
   Web of Science ®Google Scholar
• Wang WL, Castro LM, Lachos VH, Lin TI. 2019. Model-based clustering of censored data via mixtures of factor analyzers. Comput Stat Data Anal. 140:104–121.
   Web of Science ®Google Scholar
• Xie B, Pan W, Shen X. 2010. Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data. Bioinformatics. 26(4):501–508.
   PubMed Web of Science ®Google Scholar
• Yuksel SE, Wilson JN, Gader PD. 2012. Twenty years of mixture of experts. IEEE Trans Neural Netw Learn Syst. 23(8):1177–1193.
   PubMed Web of Science ®Google Scholar
• Zeller CB, Cabral CRB, Lachos VH, Benites L. 2019. Finite mixture of regression models for censored data based on scale mixtures of normal distributions. Adv Data Anal Classif. 13(1):89–116.
   Web of Science ®Google Scholar