Hierarchical Modelling: Hierarchical Linear Models for the Development of Growth Curves: An Example with Body Mass Index in Overweight/Obese Adults

Tutorials in Biostatistics

Volumn 2, Issue , 2005, Pages 95-126

  Heo, Moonseong ^a   Faith, Myles S ^b   Mott, John W ^c   Gorman, Bernard S ^d   Redden, David T ^e   Allison, David B ^e  

^a WEILL CORNELL MEDICAL COLLEGE   (United States)

^b Columbia University College of Physicians and Surgeons ^*  (United States)

^c NEW YORK STATE PSYCHIATRIC INSTITUTE   (United States)

^d NASSAU COMMUNITY COLLEGE   (United States)

^e UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

Author keywords

body mass index; growth curves; hierarchical linear model; obesity; pooling

Indexed keywords

HEALTH RISKS; NUTRITION;

BODY MASS INDEX; GROWTH CURVES; HIERARCHICAL LINEAR MODELING; OBESITY; POOLING;

RISK ASSESSMENT;

EID: 84958612076     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/0470023724.ch1b(iii)     Document Type: Chapter

References (51)

1. Aber MS, McArdle JJ. Latent growth curve approach to modelling the development of competence. In Criteria for Competence: Controversies in the Conceptualization and Assessment of Children's Abilities, Chandler M, Chapman M (eds). Lawrence Erlbaum: Hillsdale NJ, 1991; 231-258.
2. Klein DN, Norden KA, Ferro T, Leader JB, Kasch KL, Klein LM, Schwartz JE, Aronson TA. Thirty-month naturalistic follow-up study of early-onset dysthymic disorder: course, diagnostic stability, and prediction of outcome. Journal of Abnormal Psychology 1998; 107:338-348.
3. Allison DB, Paultre F, Maggio C, Mezzitis N, Pi-Sunyer FX. The use of areas under curves in diabetes research. Diabetes Care 1995; 18:245-250.
4. McArdle JJ, Allison DB. Regression change models with incomplete repeated measures data in obesity research. In Obesity Treatment: Establishing Goals, Improving Outcomes, and Reviewing the Research Agenda, Allison DB, Pi-Sunyer FX (eds). NATO ASI series, vol. 278, Plenum Press: New York, 1995; 53-64.
5. Byrk AS, Raudenbush SW. Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications: Newbury Park, CA, 1992.
6. Longford NT. Random Coeffcient Models. Oxford University Press: New York, 1993.
7. Goldstein H. Multilevel Statistical Models. 2nd edn. Wiley: New York, 1995.
8. Hox JJ. Applied Multilevel Analysis. TT-Publikaties: Amsterdam, 1995.
9. Little TD, Schnabel KU, Baumert J (eds). Modeling Longitudinal and Multilevel Data: Practical Issues, Applied Approaches, and Specific Examples. Erlbaum: Mahwah, NJ, 2000.
10. Snijders TAB, Bosker RJ. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. SAGE: London, 1999.
11. Kreft IGG, de Leeuw J. Introducing Multilevel Modeling. SAGE: Newbury Park, CA, 1998.
12. Sullivan LM, Dukes KA, Losina E. Tutorial in biostatistics: An introduction to hierarchical linear modelling. Statistics in Medicine 1999; 18:855-888.
13. Bushing F, Meijer E, van der Leeden R. MLA J Multilevel Analysis for Two Level Data, Version 3.2. Leiden University, Faculty of Social and Behavioural Sciences: Leiden, 1997.
14. Akaike H. Information theory and an extension of the entropy maximization principle. In Proceedings of the Second International Symposium on Information Theory, Petrov B, Cask R (eds). Akademica: Kiado, 1973.
15. Schwartz G. Estimating the dimension of a model. Annals of Statistics 1978; 6:461- 464.
16. Kreft IGG, de Leeuw J, van der Leen R. Review of five multilevel analysis programs: BMDP-5V, GENMOD, HLM, ML3, and VARCL. American Statistician 1994; 48:324 -335.
17. Singer JD. Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics 1998; 23:323-355.
18. Bryk AS, Raudenbush SW, Congdon RT. HLM Hierarchical Linear and Nonlinear Modeling with the HLM=2L and HLM=3L Programs. Scientific Software International Inc.: Chicago, IL, 1996.
19. Goldstein H, Rasbash J, Plewis I, Draper D, Browne W, Yang M, Woodhouse G, Healy M. A User's Guide to MLwin; version 1.0. Multilevel Models Projects, Institute of Education: London, 1998.
20. Pi-Sunyer FX. Medical hazards of obesity. Annals of Internal Medicine 1993; 119:655- 660.
21. National Heart, Lung, and Blood Institute. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report. National Heart, Lung, and Blood Institute: Bethesda, MD, 1998.
22. Kuczmarski RJ, Flegal KM, Campbell SM, Johnson CL. Increasing prevalence of overweight among US adults. The National Health and Examination Surveys. Journal of the American Medical Association 1994; 272: 205-211.
23. Michels KB, Greenland S, Rosner BA. Does body mass index adequately capture the relation of body composition and body size to health outcomes? American Journal of Epidemiology 1998; 147:167-172.
24. Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath CW Jr. Body mass index and mortality in a prospective cohort of US adults. New England Journal of Medicine 1999; 341:1097-1105.
25. Bartlett SJ, Faith MS, Fontaine KR, Cheskin LJ, Allison DB. Is the prevalence of successful weight loss and maintenance higher in the general community than the research clinic? Obesity Research 1999; 7:407- 413.
26. Brownell KD, Jefferey RW. Improving long-term weight loss: pushing the limits of treatment. Behavior Therapy 1987; 18:353-374.
27. Poston WS, Foreyt JP, Borrell L, Haddock CK. Challenges in obesity management. Southern Medical Journal 1998; 91:710 -720.
28. Plankey MW, Stevens J, Flegal KM. Prediction equations do not eliminate systematic error in self-reported body mass index. Obesity Research 1997; 5:308-314.
29. Young CH, Savola KL, Phelps E. Inventory of Longitudinal Studies in the Social Sciences. SAGE Publications: Newbury Park, CA, 1991.
30. Dawber TR. The Framingham Study. Harvard University Press: Cambridge, 1980.
31. Benfari RC. The multiple risk factor intervention trial (MRFIT): III. The model for intervention. Preventive Medicine 1981; 10:426-442.
32. MRFIT Research Group. Multiple Risk Factor Intervention Trial, risk factor changes and mortality results. Journal of the American Medical Association 1981; 248:1465-1477.
33. National Center for Health Statistic. Health and Nutrition Examination Survey I, 1971-1975 (computer file), 2nd ICPSR edn. Ann Arbor, MI: Inter-University Consortium for Political and Social Research (producer and distributor), 1983.
34. National Center for Health Statistic. Health and Nutrition Examination Survey I, Epidemiologic Follow-up Study 1982-1984 (computer file), 2nd release. Hyattsville, MD: Department of Health and Human Services, National Center for Health Statistic (producer), 1992. Ann Arbor, MI: Inter-University Consortium for Political and Social Research (producer and distributor), 1992.
35. Tecumseh Management Committee. Tecumseh Mortality Follow-up Study, 1978 (computer file), Ann Arbor, MI: Tecumseh Management Committee (producer), 1992. Ann Arbor, MI: Inter-University Consortium for Political and social research (distributor), 1993.
36. Korn EL, Graubard BI, Midthune D. Time-to-event analysis of longitudinal follow-up of a survey: choice of the time-scale. American Journal of Epidemiology 1997; 145:72-80.
37. Goldstein H. Multilevel mixed linear model analysis using iterative generalized least squares. Biometrika 1986; 73:43-56.
38. Everitt BS. The Cambridge Dictionary of Statistics. Cambridge Press: Cambridge, U.K, 1998.
39. Clayton D, Hills M. Statistical Models in Epidemiology. Oxford University Press: New York, 1993.
40. Miyazaki Y, Raudenbush SW. Tests for linkage of multiple cohorts in an accelerated longitudinal design. Psychological Methods 2000; 5:44-63.
41. Braun HI. Empirical Bayes method: a tool for exploratory analysis. In Multilevel Analysis of Educational Data, Bock RD (ed.). Academic Press: New York, 1989; 19-55.
42. Pollack BN. Hierarchical linear modelling and the 'unit of analysis' problem: a solution for analyzing responses of intact group members. Group Dynamics: Theory, Research, and Practice 1998; 2;299-312.
43. Kreft IGG (ed.). Hierarchical linear models: Problems and prospects. Journal of Educational and Behavioral Statistics 1995; 20(2) (special issue).
44. Liang K, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:45-51.
45. Zeger SL, Karim MR. Generalized linear models with random effects; a Gibbs sampling approach. Journal of the American Statistical Association 1991; 86:79-86.
46. Normand SL. Tutorial in Biostatistics. Meta-analysis: Formulating, evaluating, combining, and reporting. Statistics in Medicine 1999; 18;321-359.
47. Cooper HM, Hedges LV (eds). The Handbook of Research Synthesis. Russell Sage Foundation: New York, 1994.
48. St. Pierre NR. Integrating quantitative findings from multiple studies using mixed model methodology. Journal of Dairy Science 2001; 84:741-745.
49. Williamson DF, Kahn HS, Remington PL, Anda RF. The 10-year incidence of overweight and major weight gain in US adults. Archives of Internal Medicine 1990; 150:665-672.
50. Bjorvell H, Rossner S. A ten year follow-up of weight change in severely obese subjects treated in a behavioral modification program. International Journal of Obesity 1990; 14 (Suppl. 2):88.
51. Brownell KD. Relapse and the treatment of obesity, In Treatment of the Obese Patient, Wadden TA, VanItallie TB (eds). Guilford Press: New York, 1992; 437-455.