Bayesian Longitudinal Plateau Model of Adult Grip Strength

被引:38
作者
Nahhas, Ramzi W. [1 ]
Choh, Audrey C. [1 ]
Lee, Miryoung [1 ,2 ]
Chumlea, William M. Cameron [1 ,2 ]
Duren, Dana L. [1 ,3 ]
Siervogel, Roger M. [1 ,2 ]
Sherwood, Richard J. [1 ,2 ]
Towne, Bradford [1 ,2 ]
Czerwinski, Stefan A. [1 ]
机构
[1] Wright State Univ, Dept Community Hlth, Lifespan Hlth Res Ctr, Boonshoft Sch Med, Dayton, OH 45420 USA
[2] Wright State Univ, Dept Pediat, Boonshoft Sch Med, Dayton, OH 45420 USA
[3] Wright State Univ, Dept Orthopaed Surg, Boonshoft Sch Med, Dayton, OH 45420 USA
基金
美国国家卫生研究院;
关键词
MULTIPLE IMPUTATION; MUSCLE STRENGTH; BIRTH; SIZE; WINBUGS; WEIGHT; MASS; AGE;
D O I
10.1002/ajhb.21057
中图分类号
Q98 [人类学];
学科分类号
030303 ;
摘要
Objectives: This article illustrates the use of applied Bayesian statistical methods in modeling the trajectory of adult grip strength and in evaluating potential risk factors that may influence that trajectory Methods: The data consist of from 1 to 11 repeated grip strength measurements from each of 498 men and 533 women age 18-96 years in the Fels Longitudinal Study (Roche AF. 1992. Growth, maturation and body composition: the Fels longitudinal study 1929-1991 Cambridge. Cambridge University Press). In this analysis, the Bayesian framework was particularly useful for fitting a nonlinear mixed effects plateau model with two unknown change points and for the joint modeling of a time-varying covariate. Multiple imputation (MI) was used to handle missing values with posterior inferences appropriately adjusted to account for between-imputation variability. Results: On average, men and women attain peak grip strength at the same age (36 years), women begin to decline in grip strength sooner (age 50 years for women and 56 years for men), and men lose grip strength at a faster rate relative to their peak; there is an increasing secular trend in peak grip strength that is not attributable to concurrent secular trends in body size, and the grip strength trajectory varies with birth weight (men only), smoking (men only), alcohol consumption (men and women), and sports activity (women only). Conclusions: Longitudinal data analysis requires handling not only serial correlation but often also time-varying covariates, missing data, and unknown change points Bayesian methods, combined with MI, are useful in handling these issues. Am. J. Hum. Biol. 22.648-656, 2010. (C) 2010 Wiley-Liss, Inc
引用
收藏
页码:648 / 656
页数:9
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