Application of a hierarchical model incorporating intrafamily correlation and cluster effects

Nursing Research

Volumn 60, Issue 3, 2011, Pages 208-212

  Cheng, An Lin ^a   Kelly, Patricia J ^a  

^a UNIVERSITY OF MISSOURI KANSAS CITY   (United States)

Author keywords

cluster effect; longitudinal data; mixed modeling

Indexed keywords

ARTICLE; CHILD; HUMAN; MODEL; PARENT; RANDOMIZED CONTROLLED TRIAL (TOPIC); STATISTICAL MODEL;

CHILD; HUMANS; LIKELIHOOD FUNCTIONS; MODELS, NURSING; MODELS, STATISTICAL; PARENTS; RANDOMIZED CONTROLLED TRIALS AS TOPIC;

EID: 79955796091     PISSN: 00296562     EISSN: None     Source Type: Journal    
DOI: 10.1097/NNR.0b013e31820a3dbe     Document Type: Article

References (20)

1. Box, E. P.,&Cox,D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society, Series B, 26, 211-252.
2. Casella, G., & Berger, R. L. (2001). Statistical inference (2nd ed.). Belmont, CA: Duxbury Press.
3. Demidenko, E. (2004). Mixed models: Theory and applications. Hoboken, NJ: Wiley.
4. Draper, D. (1995). Inference and hierarchical modeling in the social sciences. Journal of Educational and Behavioral Statistics, 20, 115-147.
5. Estabrooks, C.A.,Midodzi,W.K.,Cummings, G. G., & Wallin, L. (2007). Predicting research use in nursing organizations: A multilevel analysis. Nursing Research, 56, S7-S23.
6. Feldman, H. A.,&McKinlay, S. M. (1994). Cohort versus cross-sectional design in large field trials: Precision, sample size, and a unifying model. Statistics in Medicine, 13, 61-78.
7. Greenland, S. (2000). Principles of multilevel modeling. International Journal of Epidemiology, 29, 158-167.
8. Herrera, A. P., Lee, J. W., Nanyonjo, R. D., Laufman, L. E., & Torres-Vigil, I. (2009). Religious coping and caregiver well-being in Mexican-American families. Aging & Mental Health, 13, 84-91.
9. Kelly, P. J., Lesser, J., Cheng, A. L., Oscos- Sanchez, M., Martinez, E., Pineda, D., et al. (2010). A prospective randomized controlled trial of an interpersonal violence prevention program with a Mexican- American community. Family & Community Health, 33, 207-215.
10. Knafl, G. J., Knafl, K. A., & McCorkle, R. (2005). Mixed models incorporating intra-familial correlation through spatial autoregression. Research in Nursing & Health, 28, 348-356. (Pubitemid 41366132)
11. Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963-974.
12. Liang, K. Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13-22.
13. Littell, R. C., Henry, P. R., & Ammerman, C. B. (1998). Statistical analysis of repeated measures data using SAS procedures. Journal of Animal Science, 76(4), 1216-1231. (Pubitemid 128437870)
14. Littell, R. C., Milliken, G. A., Stroup, W. W., & Wolfinger, R. D. (1996). SAS system for mixed models. Cary, NC: SAS Publishing.
15. Mood, A. M., Graybill, F. A.,&Boes, D. C. (1974). Introduction to the theory of statistics (3rd ed.). New York:McGraw-Hill.
16. Murray, D. M. (1998). Design and analysis of group-randomized trials. New York: Oxford University Press.
17. Rasbash, J., & Goldstein, H. (1994). Efficient analysis of mixed hierarchical and cross-classified random structures using a multilevel model. Journal of Educational and Behavioral Statistics, 19, 337-350.
18. Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, 23, 323-355. (Pubitemid 128474255)
19. Verbeke, G., & Molenberghs, G. (2000). Linear mixed models for longitudinal data. New York: Springer.
20. Zucker, D. M., Lakatos, E., Webber, L. S., Murray, D. M.,McKinlay, S. M., Feldman, H. A., et al. (1995). Statistical design of the child and adolescent trial for cardiovascular health (catch): Implications of cluster randomization. Controlled Clinical Trials, 16, 96-118.