Uzunlamasına kısmi-sürekli veri için marjinalleştirilmiş çok seviyeli rastgele etkili bir model

Yıl 2018, Cilt: 11 Sayı: 1, 23 - 31, 29.06.2018

Gül İnan

Öz

Bu çalışmada,
uzunlamasına kısmi-sürekli veri için marjinalleştirilmiş çok seviyeli rastgele
etkili bir model önerdik. Önerilen yöntemin başarısını farklı senaryolar altında
bir Monte Carlo benzetim çalışmasıyla inceledik. Benzetim çalışmasının sonuçları
önerilen modelin yeterli istatistiksel özelliklere sahip olduğunu göstermiştir.

Anahtar Kelimeler

Artırılmış sıfırlar, ilişkili rastgele etkiler

A marginalized multilevel random effects model for longitudinal semi-continuous data

Öz

In this study, we proposed a marginalized multilevel random effects
model for analysis of longitudinal semi-continuous data. We investigated the
performance of the proposed model through a Monte
Carlo simulation study under scenarios.
The results of the simulation study showed that the proposed model has some
favourable statistical properties.

Anahtar Kelimeler

Augmented zeros, correlated random effects, marginalized models

Kaynakça

• [1] D. Bandyopadhyay, D.M Galvis, V.H. Lachos, 2017, Augmented mixed models for clustered proportion data, Statistical Methods in Medical Research, 26(2), 880-897. [2] V.T. Farewell, D.L. Long, B.D.M. Tom, S. Yiu, L. Su, 2017, Two-part and related regression models for longitudinal data, Annual Review of Statistics and Its Application, 4, 283-315.
• [3] D.M. Galvis, D. Bandyopadhyay, V.H. Lachos, 2014, Augmented mixed beta regression models for periodontal proportion data, Statistics in Medicine, 33(21), 3759-3771.
• [4] M. Griswold, B. Swihart, B. Cao, S. Zeger, 2013, Practical marginalized multilevel models, Statistics, 2, 129-142.
• [5] P. Heagerty, 1999, Marginally specified logistic-normal models for longitudinal binary data, Biometrics, 55, 688-698.
• [6] P. Heagerty, S. Zeger, 2000, Marginalized multilevel models and likelihood inference (with discussion), Statistical Science, 15, 1-26.
• [7] G. Inan, 2018, Comments on “Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros”, Statistics in Medicine, 37, 324–326.
• [8] W. Kassahun, T. Neyens, G. Molenberghs, C. Faes, G. Verbeke, 2014, Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros, Statistics in Medicine, 33(25), 4402-4419.
• [9] K. Lee, Y. Joo, J.J. Song, D.W. Harper, 2011, Analysis of zero-inflated clustered count data: A marginalized model approach, Computational Statistics and Data Analysis, 55(1), 824-837.
• [10] B, Neelon, A.J. O'Malley, V.A. Smith, 2016, Modeling zero-modified count and semi-continuous data in health services research Part 1: Back-ground and overview, Statistics in Medicine, 35(27), 5070-5093.
• [11] B. Neelon, A.J. O'Malley, V.A. Smith, 2016, Modeling zero-modified count and semi-continuous data in health services research part 2: Case studies, Statistics in Medicine, 35(27), 5094-5112.
• [12] M.K. Olsen, J.L. Schafer, 2001, A two-part random-effects model for semi-continuous longitudinal data, Journal of the American Statistical Association, 96(454), 730-745.
• [13] M. Rodrigues-Motta, et al., 2015, A mixed-effect model for positive responses augmented by zeros, Statistics in Medicine, 34(10), 1761-1778.
• [14] V.A. Smith, B. Neelon, M.L. Maciejewski, J.S. Preisser, 2017, Two parts are better than one: modeling marginal means of semi-continuous data, Health Services and Outcomes Research Methodology, 1-21.
• [15] V.A. Smith, B. Neelon, J.S. Preisser, M.L. Maciejewski, 2017, A marginalized two-part model for longitudinal semi-continuous data, Statistical Methods in Medical Research, 26(4), 1949-1968.
• [16] L. Su, B.D. Tom, V.T. Farewell, 2015, A likelihood-based two-part marginal model for longitudinal semi- continuous data, Statistical Methods in Medical Research, 24(2), 194-205.
• [17] B.D. Tom, L. Su, V.T. Farewell, 2016, A corrected formulation for marginal inference derived from two- part mixed models for longitudinal semi-continuous data, Statistical Methods in Medical Research, 25(5), 2014-2020.
• [18] B. Zhang, W. Liu, Y. Hu, 2017, Estimating marginal and incremental effects in the analysis of medical expenditure panel data using marginalized two-part random-effects generalized Gamma models: Evidence from China healthcare cost data, Statistical Methods in Medical Research, DOI:0962280217690770.