×
Lu, Zihang; Lou, Wendy

Bayesian consensus clustering for multivariate longitudinal data. (English) Zbl 07929467

Stat. Med. 41, No. 1, 108-127 (2022).

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis

Keywords:

Bayesian consensus clustering; disease clustering; integrative clustering; mixture model; multivariate longitudinal data
Cite Review PDF
Full Text: DOI

References:

[1] KooW, KimH. Bayesian nonparametric latent class model for longitudinal data. Stat Methods Med Res. 2020;29(11):3381‐3395.
[2] LuZ, LouW. Shape invariant mixture model for clustering non‐linear longitudinal growth trajectories. Stat Methods Med Res. 2019;28(12):3769‐3784.
[3] NeelonB, SwamyGK, BurgetteLF, MirandaML. A Bayesian growth mixture model to examine maternal hypertension and birth outcomes. Stat Med. 2011;30(22):2721‐2735.
[4] De la Cruz‐MesíaR, QuintanaFA, MarshallG. Model‐based clustering for longitudinal data. Computational Statistics & Data Analysis. 2008;52(3):1441‐1457. · Zbl 1452.62454
[5] OrriM, GaleraC, TureckiG, et al. Association of childhood irritability and depressive/anxious mood profiles with adolescent suicidal ideation and attempts. JAMA Psychiat. 2018;75(5):465‐473.
[6] ChenW, SubbaraoP, McGihonRE, et al. Patterns of health care use related to respiratory conditions in early life: a birth cohort study with linked administrative data. Pediatr Pulmonol. 2019;54(8):1267‐1276.
[7] NaginDS. Group‐based trajectory modeling and criminal career research. J Res Crime Delinq. 2016;53(3):356‐371.
[8] Proust‐LimaC, PhilippsV, DiakiteA, LiquetB, ProustMC, LimaP. Package ’lcmm’ 2020.
[9] LeibyBE, SammelMD, Ten HaveTR, LynchKG. Identification of multivariate responders and non‐responders by using Bayesian growth curve latent class models. J Royal Stat Soc Ser C (Appl Stat). 2009;58(4):505‐524.
[10] SlaughterJC, HerringAH, ThorpJM. A Bayesian latent variable mixture model for longitudinal fetal growth. Biometrics. 2009;65(4):1233‐1242. · Zbl 1180.62184
[11] LimY, CheungYK, OhHS. A generalization of functional clustering for discrete multivariate longitudinal data. Stat Methods Med Res. 2020;29(11):3205‐3217.
[12] ElliottMR, GalloJJ, Ten HaveTR, BognerHR, KatzIR. Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction. Biostatistics. 2005;6(1):119‐143. · Zbl 1069.62095
[13] KomárekA, KomárkováL. Clustering for multivariate continuous and discrete longitudinal data. The Annals of Applied Statistics. 2013;7(1):177‐200. · Zbl 1454.62197
[14] KomárekA, KomárkováL. Capabilities of R package mixAK for clustering based on multivariate continuous and discrete longitudinal data. J Stat Softw. 2014;59(12):1‐38.
[15] KirkP, GriffinJE, SavageRS, GhahramaniZ, WildDL. Bayesian correlated clustering to integrate multiple datasets. Bioinformatics. 2012;28(24):3290‐3297.
[16] LockEF, DunsonDB. Bayesian consensus clustering. Bioinformatics. 2013;29(20):2610‐2616.
[17] RichardsonS, TsengGC, SunW. Statistical methods in integrative genomics. Ann Rev Stat Appl. 2016;3:181‐209.
[18] Proust‐LimaC, PhilippsV, LiquetB. Estimation of extended mixed models using latent classes and latent processes: the R package lcmm. J Stat Softw. 2017;78(2):1‐56.
[19] KassRE, RafteryAE. Bayes factors. J Am Stat Assoc. 1995;90(430):773‐795. · Zbl 0846.62028
[20] FraleyC, RafteryAE. How many clusters? which clustering method? answers via model‐based cluster analysis. Comput J. 1998;41(8):578‐588. · Zbl 0920.68038
[21] MoQ, ShenR, GuoC, VannucciM, ChanKS, HilsenbeckSG. A fully Bayesian latent variable model for integrative clustering analysis of multi‐type OMICS data. Biostatistics. 2018;19(1):71‐86.
[22] SpiegelhalterDJ, BestNG, CarlinBP, Van Der LindeA. Bayesian measures of model complexity and fit. J Royal Stat Soc Ser B (Stat Methodol). 2002;64(4):583‐639. · Zbl 1067.62010
[23] CeleuxG, ForbesF, RobertCP, TitteringtonDM. Deviance information criteria for missing data models. Bayesian Anal. 2006;1(4):651‐673. · Zbl 1331.62329
[24] Frühwirth‐SchnatterS, PyneS. Bayesian inference for finite mixtures of univariate and multivariate skew‐normal and skew‐t distributions. Biostatistics. 2010;11(2):317‐336. · Zbl 1437.62465
[25] BanfieldJD, RafteryAE. Model‐based Gaussian and non‐Gaussian clustering. Biometrics. 1993;49(3):803‐821. · Zbl 0794.62034
[26] BiernackiC, CeleuxG, GovaertG. Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans Pattern Anal Mach Intell. 2000;22(7):719‐725.
[27] WatanabeS. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J Mach Learn Res. 2010;11(Dec):3571‐3594. · Zbl 1242.62024
[28] MerkleEC, FurrD, Rabe‐HeskethS. Bayesian comparison of latent variable models: conditional versus marginal likelihoods. Psychometrika. 2019;84(3):802‐829. · Zbl 1431.62551
[29] DicksonER, GrambschPM, FlemingTR, FisherLD, LangworthyA. Prognosis in primary biliary cirrhosis: model for decision making. Hepatology. 1989;10(1):1‐7.
[30] FlemingTR, HarringtonDP. Counting Processes and Survival Analysis. Vol 169. Hoboken, NJ: John Wiley & Sons; 2011.
[31] StephensM. Dealing with label switching in mixture models. J Royal Stat Soc Ser B (Stat Methodol). 2000;62(4):795‐809. · Zbl 0957.62020
[32] HennigC. Cluster‐wise assessment of cluster stability. Comput Stat Data Anal. 2007;52(1):258‐271. · Zbl 1452.62447
[33] HubertL, ArabieP. Comparing partitions. J Classif. 1985;2(1):193‐218.
[34] RousseauJ, MengersenK. Asymptotic behaviour of the posterior distribution in overfitted mixture models. J Royal Stat Soc Ser B (Stat Methodol). 2011;73(5):689‐710. · Zbl 1228.62034
[35] Van HavreZ, WhiteN, RousseauJ, MengersenK. Overfitting Bayesian mixture models with an unknown number of components. PLoS One. 2015;10(7):e0131739.
[36] RichardsonS, GreenPJ. On Bayesian analysis of mixtures with an unknown number of components (with discussion). J Royal Stat Soc Ser B (Stat Methodol). 1997;59(4):731‐792. · Zbl 0891.62020
[37] StephensM. Bayesian analysis of mixture models with an unknown number of components—an alternative to reversible jump methods. Ann Stat. 2000;28(1):40‐74. · Zbl 1106.62316
[38] EscobarMD. Estimating normal means with a Dirichlet process prior. J Am Stat Assoc. 1994;89(425):268‐277. · Zbl 0791.62039
[39] EscobarMD, WestM. Bayesian density estimation and inference using mixtures. J Am Stat Assoc. 1995;90(430):577‐588. · Zbl 0826.62021
[40] MillerJW, HarrisonMT. Mixture models with a prior on the number of components. J Am Stat Assoc. 2018;113(521):340‐356. · Zbl 1398.62066
[41] GaoLL, BienJ, WittenD. Are clusterings of multiple data views independent?Biostatistics. 2020;21(4):692‐708.
[42] LuZ, LouW. Bayesian approaches to variable selection: a comparative study from practical perspectives. Int J Biostat. 2021.
[43] LeischF.Flexmix: a general framework for finite mixture models and latent glass regression in R; 2004.
[44] ShahbabaB, NealR. Nonlinear models using Dirichlet process mixtures. J Mach Learn Res. 2009;10(8):1829‐1850. · Zbl 1235.62069
[45] MolitorJ, PapathomasM, JerrettM, RichardsonS. Bayesian profile regression with an application to the national survey of children’s health. Biostatistics. 2010;11(3):484‐498. · Zbl 1437.62560
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.