Inference of nested variance components in a longitudinal myopia intervention trial

Statistics in Medicine

Volumn 24, Issue 21, 2005, Pages 3251-3267

  Hsiao, Chuhsing Kate ^a   Tsai, Miao Yu ^a   Chen, Ho Min ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Correlation; Nested repeated measurements; REML; Schwarz criterion

Indexed keywords

ATROPINE;

AGE DISTRIBUTION; ALGORITHM; ARTICLE; CHILD; CLINICAL TRIAL; COMPUTER PROGRAM; CORRELATION ANALYSIS; COVARIANCE; DISEASE COURSE; DRUG EFFICACY; FEMALE; GROUPS BY AGE; HUMAN; LONGITUDINAL STUDY; MALE; MATHEMATICAL COMPUTING; MYOPIA; SEX RATIO; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE;

ANALYSIS OF VARIANCE; ATROPINE; CHILD; COMPUTER SIMULATION; DOUBLE-BLIND METHOD; EYEGLASSES; FEMALE; HUMANS; LONGITUDINAL STUDIES; MALE; MODELS, STATISTICAL; MYDRIATICS; MYOPIA, DEGENERATIVE; RANDOMIZED CONTROLLED TRIALS;

EID: 27644473696     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/sim.2211     Document Type: Article

References (25)

1. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics 1982; 38:963-974.
2. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrics 1986; 73:13-22.
3. Jennrich RI, Schluchter MD. Unbalanced repeated-measures models with structured covariance matrices. Biometrics 1986; 42:805-820.
4. Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics 1988; 44:1049-1060.
5. Feng Z, McCulloch CE. Statistical inference using maximum likelihood estimation and the generalized likelihood ratio when the true parameter is on the boundary of the parameter space. Statistics and Probability Letters 1992; 13:325-332.
6. Stram DO, Lee JW. Variance components testing in the longitudinal mixed effects model. Biometrics 1994; 50:1171-1177.
7. Crainiceanu CM, Ruppert D. Likelihood ratio tests in linear mixed models with one variance component. Journal of the Royal Statistical Society, Series B 2004; 66:165-185.
8. Park T, Lee YJ. Covariance models for nested repeated measures data: analysis of ovarian steroid secretion data. Statistics in Medicine 2002; 21:143-164.
9. Galecki AT. General class of covariance structures for two or more repeated factors in longitudinal data analysis. Communications in Statistics: Theory and Methods 1994; 23:3105-3119.
10. Shih YF, Hsiao CK, Chen CJ, Chang CW, Hung PT, Lin LLK. An intervention trial on efficacy of atropine and multi-focal glasses in controlling myopic progression. Acta Ophthalmologica Scandinavica 2001; 79:233-236.
11. Law NM, Chew SJ, Ritch R, Thompson HW, Beuerman RW. Survey of refraction in a Chinese population shows that myopia severity can be predicted from its age of onset. (Abstract) Investigative Ophthalmology and Visual Science 1992; 33:709.
12. Lin LLK, Shih YF, Tsai CB, Chen CJ, Lee LA, Hung PT. Epidemiologic study of ocular refraction among school children in Taiwan in 1995. Optometry and Vision Science 1999; 76:275-281.
13. Jensen H. Myopia progression in young school children. Acta Ophthalmogial (Copenhagen) 1991; 69(Suppl.):200.
14. Saw SM, Katz J, Schein OD, Chew SJ, Chan TK. Epidemiology of myopia. Epidemiologic Reviews 1996; 18:175-187.
15. Laird NM, Lange N, Stram D. Maximum likelihood computations with repeated measures: application of the EM algorithm. Journal of the American Statistical Association 1987; 87:97-105.
16. Chen HM. Inference of correlation in mixed effects models for longitudinal studies. Master Thesis, Division of Biostatistics, Institute of Epidemiology, National Taiwan University, 2002
17. Schwarz G. Estimating the dimension of a model. The Annals of Statistics 1978; 6:461-464.
18. Verbeke G, Molenberghs G. The use of score tests for inference on variance components. Biometrics 2003; 59:254-262.
19. Korn EL, Graubard BI. Estimating variance components by using survey data. Journal of the Royal Statistical Society, Series B 2003; 65:175-190.
20. Louis TA. Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, Series B 1982; 44:226-233.
21. Meilijson I. A fast improvement to the EM algorithm on its own terms. Journal of the Royal Statistical Society, Series B 1989; 51:127-138.
22. Meng XL, Rubin DB. Using EM to obtain asymptotic variance-covariance matrices: the SEM algorithm. Journal of the American Statistical Association 1991; 86:899-909.
23. Natarajan R, Kass RE. Reference Bayesian methods for generalized linear mixed models. Journal of the American Statistical Association 2000; 95:227-237.
24. Pauler DK, Wakefield JC, Kass RE. Bayes factor and approximations for variance component models. Journal of the American Statistical Association 1999; 94:1242-1253.
25. Wolfinger RD, Kass RE. Nonconjugate Bayesian analysis of variance component models. Biometrics 2000; 56:768-774.