Optimal designs for empirical bayes estimators of individual linear and quadratic growth curves in linear mixed models

Statistical Methods in Medical Research

Volumn 18, Issue 4, 2009, Pages 397-419

  Candel, Math J J M ^a  

^a MAASTRICHT UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

IMIPRAMINE;

ANALYSIS OF VARIANCE; ANALYTICAL ERROR; ARTICLE; BAYES THEOREM; BUDGET; CORRELATION COEFFICIENT; COST BENEFIT ANALYSIS; DEPRESSION; DRUG COST; EMPIRICISM; GROWTH CURVE; HUMAN; MATHEMATICAL ANALYSIS; PREDICTION; STATISTICAL MODEL; TIME;

ANTIDEPRESSIVE AGENTS, TRICYCLIC; BAYES THEOREM; DEPRESSION; HUMANS; IMIPRAMINE; LINEAR MODELS; RESEARCH DESIGN; TREATMENT OUTCOME;

EID: 68449102948     PISSN: 09622802     EISSN: None     Source Type: Journal    
DOI: 10.1177/0962280207088026     Document Type: Article

References (33)

1. Albert PS Tutorial in biostatistics: Longitudinal data analysis (repeated measures) in clinical trials. Statistics in Medicine 1999; 18: 1707-32.
2. Bours GJJW, Halfens RJG, Candel MJJM, Grol RTPM, Huijer Abu-Saad H. A pressure ulcer audit and feedback project across multi-hospital settings in the Netherlands. The International Journal for Quality in Health Care 2004; 16: 211-18.
3. Hedeker D., Gibbons RD MIXREG: A computer program for mixed-effects regression analysis with autocorrelated errors. Computer Methods and Programs in Biomedicine 1996; 49: 229-52.
4. Reisby N., Gram LF, Bech P. et al. Imipramine: Clinical effects and pharmacokinetic variability. Psychopharmacology 1977; 54: 263-72.
5. Raudenbush SW, Xiao-Feng L. Effects of study duration, frequency of observation, and sample size on power in studies of group differences in polynomial change. Psychological Methods 2001; 6: 387-401.
6. Bock RD Multivariate statistical methods in behavioral research. McGraw-Hill, New York; 1975.
7. Heo M., Faith MS, Mott JW, Gorman BS, Redden DT, Allison DB Tutorial in biostatistics. Hierarchical linear models for the development of growth curves: An example with body mass index in overweight/obese adults. Statistics in Medicine 2003; 22: 1911-42.
8. Van Dommelen P., Van Buuren S., Zandwijken GRJ, Verkerk PH Individual growth curve models for assessing evidence-based referral criteria in growth monitoring. Statistics in Medicine 2005; 24: 3663-74.
9. Lyles RH, Xu, J. Classifying individuals based on predictors of random effects. Statistics in Medicine 1999; 18: 35- 52.
10. Kriström B., Wikland KA Growth prediction models: Concepts and use. Hormone research 2002; 57(suppl): 66-70.
11. Chvatal BA, Behrents RG, Ceen RF, Buschang PH Development and testing of multilevel models for longitudinal craniofacial growth prediction. American Journal of Orthodontics and Dentofacial Orthopedics 2005; 128: 45-56.
12. Cook NR Estimating predictive values for blood pressure measurements from multivariate regression models with covariates. Statistics in Medicine 1996; 15: 2013-28.
13. Ware JH, Wu MC Tracking: Prediction of future values from serial measurements. Biometrics 1981; 37: 427-37.
14. Darby SC, Fearn T. The Chatham blood pressure study. An application of Bayesian growth curve models to a longitudinal study of blood pressure in children. International Journal of Epidemiology 1979; 8: 15-21.
15. Verbeke G., Molenberghs G. Linear mixed models for longitudinal data. Springer; New York: 2000.
16. Searle SR, Casella G., McCulloch CE Variance components. Wiley, New York; 1992.
17. Robinson, GK That BLUP is a good thing: The estimation of random effects. Statistical Science 1991; 6: 15-32.
18. Maritz JS, Lwin T. Empirical bayes methods. Chapman and Hall, London; 1989.
19. Candel Mjjm, Winkens B. Performance of empirical Bayes estimators of level-2 random parameters in multilevel analysis: A Monte Carlo study for longitudinal designs. Journal of Educational and Behavioral Statistics 2003; 28: 169-94.
20. Galbraith S., Marschner IC Guidelines for the design of clinical trials with longitudinal outcomes. Controlled Clinical Trials 2002; 23: 257-73.
21. Winkens B., Schouten HJA, Van Breukelen GJP, Berger MPF. Optimal time-points in clinical trials with linearly divergent treatment effects. Statistics in Medicine 2005; 24: 3743-56.
22. Winkens B., Schouten HJA, Van Breukelen GJP, Berger MPF. Optimal number of repeated measures and group sizes in clinical trials with linearly divergent treatment effects. Contemporary Clinical Trials 2006; 27: 57-69.
23. Bryk AS, Raudenbush SW Hierarchical linear models: Applications and data analysis methods. Sage; Newbury Park, 1992.
24. Rao CR Simultaneous estimation of parameters in different linear models and applications to biometric problems. Biometrics 1975; 31: 545-54.
25. Taylor JMG, Lay N. Does the covariance structure matter in longitudinal modelling for the prediction of future CD4 counts? Statistics in Medicine 1998; 17: 2381-94.
26. Chaloner K., Verdinelli I. Bayesian experimental design: A review. Statistical Science 1995; 10: 273-304.
27. Verdinelli I. A note on Bayesian design for the normal linear model with unknown error variance. Biometrika 2000; 87: 222-27.
28. Box GEP. Problems in the analysis of growth and wear curves. Biometrics 1950; 6: 362-89.
29. Singer JD Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics 1998; 24: 323-55.
30. Moerbeek M., Van Breukelen GJP, Berger MPF. Design issues for experiments in multilevel populations. Journal of Educational and Behavioral Statistics 2000; 25: 271-84.
31. Raudenbush SW, Xiao-Feng L. Statistical power and optimal design for multisite randomized trials. Psychological Methods 2000; 5: 199-213.
32. Tan ES, Berger MPF. Optimal allocation of time points for the random effects model. Communications in Statistics: Simulation and Computation 1999; 28: 517-40.
33. Rao CR Prediction of future observations with special reference to linear models. In: Krishnaiah PR ed. Multivariate analysis - IV. North-Holland Publishing Company, 1997: 193-208.