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Composite marginal quantile regression analysis for longitudinal adolescent body mass index data
Author(s) -
Yang ChiChuan,
Chen YiHau,
Chang HsingYi
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7355
Subject(s) - quantile , quantile regression , statistics , overweight , body mass index , regression analysis , econometrics , longitudinal study , regression , composite index , correlation , mathematics , medicine , composite indicator , pathology , geometry
Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long‐term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai ( Biometrics 2016; 72 :74–84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness‐of‐fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd.