Simultaneous Bayesian modelling of skew-normal longitudinal measurements with non-ignorable dropout

Oludare Ariyo1, Matthew A. Adeleke2
1Department of Statistics, Federal University of Agriculture, Abeokuta, Nigeria
2Discipline of Genetics, School of Life Sciences, University of Kwa-Zulu Natal, Westville, Durban, South Africa

Tóm tắt

Từ khóa


Tài liệu tham khảo

Adeleke MA, Peters SO, Ozoje MO, Ikeobi CON, Bamgbose AM, Adebambo OA (2011) Growth performance of Nigerian local chickens in crosses involving an exotic broiler breeder. Trop Anim Health Prod 43(3):643–650. https://doi.org/10.1007/s11250-010-9747-3

Alsefri M, Sudell M, García-Fiñana M, Kolamunnage-Dona R (2020) Bayesian joint modelling of longitudinal and time to event data: a methodological review. BMC Med Res Methodol 20:1–17

Arellano-Valle R, Bolfarine H, Lacho V (2007) Bayesian inference for skew-normal linear mixed models. J Appl Stat 34(4):663–682

Ariyo O, Lesaffre E, Verbeke G, Quintero A (2019) Model selection for Bayesian linear mixed models with longitudinal data: sensitivity to the choice of priors. Commun Stat Simul Comput. https://doi.org/10.1080/03610918.2019.1676439

Ariyo O, Quintero A, Muñoz J, Verbeke G, Lesaffre E (2019b) Bayesian model selection in linear mixed models for longitudinal data. J Appl Stat 47(5):890–913

Azzalini A, Capitanio A (1999) Statistical applications of the multivariate skew normal distribution. J Roy Stat Soc Ser B (Stat Methodol) 61(3):579–602

Baghfalaki T, Ganjali M (2015) A Bayesian approach for joint modeling of skew-normal longitudinal measurement and time to event data. REVSTAT Stat J 13(2):169–191

Bogaerts K, Komarek A, Lesaffre E (2017) Survival analysis with interval-censored data: a practical approach with examples in R, SAS, and BUGS. CRC Press, London

Brooks SP, Gelman A (1998) General methods for monitoring convergence of iterative simulations. J Comput Graph Stat 7(4):434–455

Celeux G, Forbes F, Robert C, Titterington D (2006) Deviance information criteria for missing data models. Bayesian Anal 1:651–706

Chan J (2016) Bayesian informative dropout for longitudinal binary data with random effects using conditional and joint modeling approaches. Biom J 58:549–569

Chan J, Grant A (2016) On the observed-data deviance information criterion for volatility modeling. J Financ Economet 14(4):772–802

Cox DR (1972) Regression models and life-tables. J Roy Stat Soc Ser B (Methodol) 34(2):187–202

Diggle P, Kenward MG (1994) Informative drop-out in longitudinal data analysis. J Roy Stat Soc Ser C (Appl Stat) 43(1):49–73

Fitmaurice G, Molenberghs G, Lipsitz S (1995) Regression models for longitudinal binary responses with informative drop-outs. J Roy Stat Soc 57(B):691–704

Gelman A, Rubin DB et al (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):457–472

Henderson R, Diggle P, Dobson A (2000) Joint modelling of longitudinal measurements and event time data. Biostatistics 1(4):465–480. https://doi.org/10.1093/biostatistics/1.4.465

Hickey GL, Philipson P, Jorgensen A, Kolamunnage-Dona R (2016) Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues. BMC Med Res Methodol 16(1):117

Hobert JP, Casella G (1996) The effect of improper priors on Gibbs sampling in hierarchical linear mixed models. J Am Stat Assoc 91(436):1461–1473

Huang Y, Dagne G (2011) A Bayesian approach to joint mixed-effects models with a skwe-normal distribution and measurement errors in covariates. Biometrics 67:260–269

Huang X, Li G, Elashoff RM (2010) A joint model of longitudinal and competing risks survival data with heterogeneous random effects and outlying longitudinal measurements. Stat Interface 3(2):185

Ibrahim JG, Chu H, Chen LM (2010) Basic concepts and methods for joint models of longitudinal and survival data. J Clin Oncol 28(16):2796

Komarek A, Lesaffre E (2008) Generalized linear mixed model with a penalized gaussian mixture as a random effects distribution. Comput Stat Data Anal 52(7):3441–3458

Laird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics 38:963–974

Lesaffre E, Lawson A (2012) Bayesian biostatistics. Wiley, London

Li Q, Su L (2017) Accommodating informative dropout and death: a joint modelling approach for longitudinal and semi competing risks data. J Roy Stat Soc 66(Series C):1–18

Li N, Elashoff RM, Li G (2009) Robust joint modeling of longitudinal measurements and competing risks failure time data. Biomet J Math Methods Biosci 51(1):19–30

Ma J, Plesken H, Treisman JE, Edelman-Novemsky I, Ren M (2004) Lightoid and Claret: a rab GTPase and its putative guanine nucleotide exchange factor in biogenesis of Drosophila eye pigment granules. Proc Natl Acad Sci 101(32):11652–11657

Mchunu NN, Mwambi HG, Reddy T, Yende-Zuma N, Naidoo K (2020) Joint modelling of longitudinal and time-to-event data: an illustration using CD4 count and mortality in a cohort of patients initiated on antiretroviral therapy. BMC Infect Dis 20:1–9

Meng X (2009) Discussion of Spiegelhalter et al. J Roy Stat Soc B 64(4):633–637

Molenberghs G, Kenward MG, Lesaffre E (1997) The analysis of longitudinal ordinal data with nonrandom drop-out. Biometrika 84(1):33–44

Plummer M et al (2003) JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. In: Proceedings of the 3rd international workshop on distributed statistical computing, vol 124. Austria, pp 1–10

Quintero A, Lesaffre E (2017) Comparing latent variable models via the observed deviance information criterion (Submitted)

Rizopoulos D (2012) Joint models for longitudinal and time-to-event data: with applications in R

Rubin D (1976) Inference and missing data. Biometrics 63:581–592

Sahu S, Dey D, Branco M (2003) A new class of multivariate skew distributions with applications to Bayesian regression models. Canad J Stat 31(2):129–150

Sinha D, Chen M-H, Ghosh SK (1999) Bayesian analysis and model selection for interval-censored survival data. Biometrics 55(2):585–590

Spiegelhalter DJ, Best NG, Carlin BP, Van der Linde A (2002) Bayesian measures of model complexity and fit (with discussion). J Roy Stat Soc B 64(4):583–616

Todem D, Kim K, Fine J, Peng L (2010) Semiparametric regression models and sensitivity analysis of longitudinal data with non-random dropouts. Stat Neerl 64(2):133–156

Vaida F, Xu R (2000) Proportional hazards model with random effects. Stat Med 19:3309–3324

Verbeke G, Lesaffre E (1997) A linear mixed-effect model with heterogeneity in the random-effects population. J Am Stat Assoc 91(3):217–221

Wang C, Douglas J, Anderson S (2002) Item response models for joint analysis of quality of life and survival. Stat Med 21(1):129–142

Wulfsohn MS, Tsiatis AA (1997) A joint model for survival and longitudinal data measured with error. Biometrics 330–339

Yu B (2010) A Bayesian MCMC approach to survival analysis with doubly-censored data. Comput Stat Data Anal 54(8):1921–1929

Zhang G, Yuan Y (2012) Bayesian modeling longitudinal dydic data with nonignorable dropout, with application to a breast cancer study. Ann Appl Stat 6(2):753–771