Hierarchical Modelling

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

Moonseong Heo, Myles S. Faith, John W. Mott, Bernard S. Gorman, David T. Redden, David B. Allison

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

When data are available on multiple individuals measured at multiple time points that may vary in number or inter-measurement interval, hierarchical linear models (HLM) may be an ideal option. The present paper offers an applied tutorial on the use of HLM for developing growth curves depicting natural changes over time. We illustrate these methods with an example of body mass index (BMI; kg=m2) among overweight and obese adults. We modelled among-person variation in BMI growth curves as a function of subjects' baseline characteristics. Specifically, growth curves were modelled with two-level observations, where the first level was each time point of measurement within each individual and the second level was each individual. Four longitudinal databases with measured weight and height met the inclusion criteria and were pooled for analysis: the Framingham Heart Study (FHS); the Multiple Risk Factor Intervention Trial (MRFIT); the National Health and Nutritional Examination Survey I (NHANES-I) and its follow-up study; and the Tecumseh Mortality Follow-up Study (TMFS). Results indicated that significant quadratic patterns of the BMI growth trajectory depend primarily upon a combination of age and baseline BMI. Specifically, BMI tends to increase with time for younger people with relatively moderate obesity (25≤BMI

Original languageEnglish (US)
Title of host publicationStatistical Modelling of Complex Medical Data
Publisherwiley
Pages95-126
Number of pages32
Volume2
ISBN (Print)9780470023723, 0470023708, 9780470023709
DOIs
StatePublished - Aug 30 2005
Externally publishedYes

Fingerprint

Hierarchical Linear Models
Hierarchical Modeling
Growth Curve
Baseline
Obesity
Risk Factors
Mortality
Person
Health
Inclusion
Vary
Tend
Trajectory
Interval

Keywords

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

ASJC Scopus subject areas

  • Mathematics(all)

Cite this

Heo, M., Faith, M. S., Mott, J. W., Gorman, B. S., Redden, D. T., & Allison, D. B. (2005). Hierarchical Modelling: Hierarchical Linear Models for the Development of Growth Curves: An Example with Body Mass Index in Overweight/Obese Adults. In Statistical Modelling of Complex Medical Data (Vol. 2, pp. 95-126). wiley. https://doi.org/10.1002/0470023724.ch1b(iii)

Hierarchical Modelling : Hierarchical Linear Models for the Development of Growth Curves: An Example with Body Mass Index in Overweight/Obese Adults. / Heo, Moonseong; Faith, Myles S.; Mott, John W.; Gorman, Bernard S.; Redden, David T.; Allison, David B.

Statistical Modelling of Complex Medical Data. Vol. 2 wiley, 2005. p. 95-126.

Research output: Chapter in Book/Report/Conference proceedingChapter

Heo, M, Faith, MS, Mott, JW, Gorman, BS, Redden, DT & Allison, DB 2005, Hierarchical Modelling: Hierarchical Linear Models for the Development of Growth Curves: An Example with Body Mass Index in Overweight/Obese Adults. in Statistical Modelling of Complex Medical Data. vol. 2, wiley, pp. 95-126. https://doi.org/10.1002/0470023724.ch1b(iii)
Heo M, Faith MS, Mott JW, Gorman BS, Redden DT, Allison DB. Hierarchical Modelling: Hierarchical Linear Models for the Development of Growth Curves: An Example with Body Mass Index in Overweight/Obese Adults. In Statistical Modelling of Complex Medical Data. Vol. 2. wiley. 2005. p. 95-126 https://doi.org/10.1002/0470023724.ch1b(iii)
Heo, Moonseong ; Faith, Myles S. ; Mott, John W. ; Gorman, Bernard S. ; Redden, David T. ; Allison, David B. / Hierarchical Modelling : Hierarchical Linear Models for the Development of Growth Curves: An Example with Body Mass Index in Overweight/Obese Adults. Statistical Modelling of Complex Medical Data. Vol. 2 wiley, 2005. pp. 95-126
@inbook{92de247a441e45e899fa9fb84104e70f,
title = "Hierarchical Modelling: Hierarchical Linear Models for the Development of Growth Curves: An Example with Body Mass Index in Overweight/Obese Adults",
abstract = "When data are available on multiple individuals measured at multiple time points that may vary in number or inter-measurement interval, hierarchical linear models (HLM) may be an ideal option. The present paper offers an applied tutorial on the use of HLM for developing growth curves depicting natural changes over time. We illustrate these methods with an example of body mass index (BMI; kg=m2) among overweight and obese adults. We modelled among-person variation in BMI growth curves as a function of subjects' baseline characteristics. Specifically, growth curves were modelled with two-level observations, where the first level was each time point of measurement within each individual and the second level was each individual. Four longitudinal databases with measured weight and height met the inclusion criteria and were pooled for analysis: the Framingham Heart Study (FHS); the Multiple Risk Factor Intervention Trial (MRFIT); the National Health and Nutritional Examination Survey I (NHANES-I) and its follow-up study; and the Tecumseh Mortality Follow-up Study (TMFS). Results indicated that significant quadratic patterns of the BMI growth trajectory depend primarily upon a combination of age and baseline BMI. Specifically, BMI tends to increase with time for younger people with relatively moderate obesity (25≤BMI",
keywords = "body mass index, growth curves, hierarchical linear model, obesity, pooling",
author = "Moonseong Heo and Faith, {Myles S.} and Mott, {John W.} and Gorman, {Bernard S.} and Redden, {David T.} and Allison, {David B.}",
year = "2005",
month = "8",
day = "30",
doi = "10.1002/0470023724.ch1b(iii)",
language = "English (US)",
isbn = "9780470023723",
volume = "2",
pages = "95--126",
booktitle = "Statistical Modelling of Complex Medical Data",
publisher = "wiley",

}

TY - CHAP

T1 - Hierarchical Modelling

T2 - Hierarchical Linear Models for the Development of Growth Curves: An Example with Body Mass Index in Overweight/Obese Adults

AU - Heo, Moonseong

AU - Faith, Myles S.

AU - Mott, John W.

AU - Gorman, Bernard S.

AU - Redden, David T.

AU - Allison, David B.

PY - 2005/8/30

Y1 - 2005/8/30

N2 - When data are available on multiple individuals measured at multiple time points that may vary in number or inter-measurement interval, hierarchical linear models (HLM) may be an ideal option. The present paper offers an applied tutorial on the use of HLM for developing growth curves depicting natural changes over time. We illustrate these methods with an example of body mass index (BMI; kg=m2) among overweight and obese adults. We modelled among-person variation in BMI growth curves as a function of subjects' baseline characteristics. Specifically, growth curves were modelled with two-level observations, where the first level was each time point of measurement within each individual and the second level was each individual. Four longitudinal databases with measured weight and height met the inclusion criteria and were pooled for analysis: the Framingham Heart Study (FHS); the Multiple Risk Factor Intervention Trial (MRFIT); the National Health and Nutritional Examination Survey I (NHANES-I) and its follow-up study; and the Tecumseh Mortality Follow-up Study (TMFS). Results indicated that significant quadratic patterns of the BMI growth trajectory depend primarily upon a combination of age and baseline BMI. Specifically, BMI tends to increase with time for younger people with relatively moderate obesity (25≤BMI

AB - When data are available on multiple individuals measured at multiple time points that may vary in number or inter-measurement interval, hierarchical linear models (HLM) may be an ideal option. The present paper offers an applied tutorial on the use of HLM for developing growth curves depicting natural changes over time. We illustrate these methods with an example of body mass index (BMI; kg=m2) among overweight and obese adults. We modelled among-person variation in BMI growth curves as a function of subjects' baseline characteristics. Specifically, growth curves were modelled with two-level observations, where the first level was each time point of measurement within each individual and the second level was each individual. Four longitudinal databases with measured weight and height met the inclusion criteria and were pooled for analysis: the Framingham Heart Study (FHS); the Multiple Risk Factor Intervention Trial (MRFIT); the National Health and Nutritional Examination Survey I (NHANES-I) and its follow-up study; and the Tecumseh Mortality Follow-up Study (TMFS). Results indicated that significant quadratic patterns of the BMI growth trajectory depend primarily upon a combination of age and baseline BMI. Specifically, BMI tends to increase with time for younger people with relatively moderate obesity (25≤BMI

KW - body mass index

KW - growth curves

KW - hierarchical linear model

KW - obesity

KW - pooling

UR - http://www.scopus.com/inward/record.url?scp=84958612076&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958612076&partnerID=8YFLogxK

U2 - 10.1002/0470023724.ch1b(iii)

DO - 10.1002/0470023724.ch1b(iii)

M3 - Chapter

SN - 9780470023723

SN - 0470023708

SN - 9780470023709

VL - 2

SP - 95

EP - 126

BT - Statistical Modelling of Complex Medical Data

PB - wiley

ER -