SavedAdd your journal

Journals / Hacettepe Journal of Mathematics and Statistics / 2020 / Cilt: 49 - Sayı: 2

An adaptation of pseudo-score confidence interval method for linear mixed models

Hatice Tül Kübra Akdur
Deniz Özonur
Hülya Bayrak
Pages
902–913
Publication Date
2022-10-18
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

A depression study is analyzed for demonstrating the adaptation method.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:We adapt a confidence interval method based on a generalized Chi-Square test for fixed effect parameters of linear mixed models.
📊
Key Findings & Data:Under different correlation structure of a response variable of a linear mixed model, the performances of the adaptation method pseudo-score and some of the existing confidence interval methods are investigated by carrying out a Monte Carlo simulation study.
💡
Conclusion & Impact:A depression study is analyzed for demonstrating the adaptation method.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:linear mixed models · pseudo-score · profile likelihood

Abstract

We adapt a confidence interval method based on a generalized Chi-Square test for fixed effect parameters of linear mixed models. Under different correlation structure of a response variable of a linear mixed model, the performances of the adaptation method pseudo-score and some of the existing confidence interval methods are investigated by carrying out a Monte Carlo simulation study. The simulation study suggests that pseudo-score method provides better results for small to moderate sample size cases with dependent observations in terms of coverage probability rates and average expected lengths. A depression study is analyzed for demonstrating the adaptation method.

🔖

Cite This Article

Akdur, H. T. K., Özonur, D., Bayrak, H. (2022). An adaptation of pseudo-score confidence interval method for linear mixed models. Hacettepe Journal of Mathematics and Statistics, Cilt: 49 - Sayı: 2, 902–913. https://ajindex.com/articles/529639

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (17)

  1. A. Agresti and E. Ryu, Pseudo-score confidence intervals for parameters in discrete statistical models, Biometrika 97 (1), 215-222, 2010
  2. H.T.K. Akdur, D. Ozonur and H. Bayrak, A Comparison of Confidence Interval Methods of Fixed Effect in Nested Error Regression Model, SDU J Nat Appl Sci 20 (2), 2016
  3. M.K. Bahçecitapar, Some factors affecting statistical power of approximate tests in the linear mixed model for longitudinal data, Comm. Statist. Simulation Comput. 47 (1), 2018
  4. D. Bates, M. Machler, B. Bolker and S. Walker, Fitting linear mixed-effects models using lme4, arXiv preprint 1406.5823, 2014
  5. A.T. Beck, R.A. Steer and G.K. Brown, Beck depression inventory-II, Psychological Corporation 78 (2), 490-498, San Antonio, TX, 1996
  6. D.K. Bhaumik, A. Roy, S. Aryal, K. Hur, N. Duan, S.L.T. Normand, C.H. Brown, and R.D. Gibbons, Sample size determination for studies with repeated continuous outcomes, Psychiatr. Ann 38 (12), 2008
  7. A. R. De Leon and K. C. Chough, Analysis of mixed data: methods and applications, CRC Press, 2013
  8. D. Eugene, Mixed Models: Theory and Applications, Hoboken: Wiley, 2004
  9. G.M. Fitzmaurice, N.M. Laird and J.H.Ware Applied longitudinal analysis (Vol. 998), John Wiley and Sons, 2012
  10. A. Genz, F. Bretz, T. Miwa,., and M.T. Hothorn, Package mvtnorm, J. Stat. Softw. 11, 950-971, 2020
  11. U. Halekoh and S. Højsgaard, A kenward-roger approximation and parametric bootstrap methods for tests in linear mixed modelsthe R package pbkrtest, J. Stat. Softw. 59 (9), 1-30, 2014
  12. P.R. Houck, S. Mazumdar, T. Koru-Sengul, G. Tang, B.H. Mulsant, B.G. Pollock and C.F. Reynolds III, Estimating treatment effects from longitudinal clinical trial data with missing values: comparative analyses using different methods, Psychiatry Research 129 (2), 209-215, 2004
  13. R.N. Kackar and D.A. Harville, Approximations for standard errors of estimators of fixed and random effects in mixed linear models, J. Am. Stat. Assoc 79 (388), 853-862, 1984
  14. M. G. Kenward and J. H. Roger, Small sample inference for fixed effects from restricted maximum likelihood, Biometrics 53 (3), 983-997, 1997
  15. S. Landau, A handbook of statistical analyses using SPSS, CRC, 2004
  16. J. Liu, Statistical inference for functions of the parameters of a linear mixed model, Unpublished doctoral dissertation, Iowa State University, 2013
  17. G. Lovison, On rao score and pearson x2 statistics in generalized linear models, Statist. Papers