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Conditional random slope: A new approach for estimating individual child growth velocity in epidemiological research
Author(s) -
Leung Michael,
Bassani Diego G.,
RacinePoon Amy,
Goldenberg Anna,
Ali Syed Asad,
Kang Gagandeep,
Premkumar Prasanna S.,
Roth Daniel E.
Publication year - 2017
Publication title -
american journal of human biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.559
H-Index - 81
eISSN - 1520-6300
pISSN - 1042-0533
DOI - 10.1002/ajhb.23009
Subject(s) - statistics , concordance , random effects model , correlation , econometrics , metric (unit) , mathematics , leverage (statistics) , poisson regression , linear regression , longitudinal data , medicine , computer science , data mining , population , meta analysis , operations management , geometry , environmental health , economics
Objectives Conditioning child growth measures on baseline accounts for regression to the mean (RTM). Here, we present the “conditional random slope” (CRS) model, based on a linear‐mixed effects model that incorporates a baseline‐time interaction term that can accommodate multiple data points for a child while also directly accounting for RTM. METHODS In two birth cohorts, we applied five approaches to estimate child growth velocities from 0 to 12 months to assess the effect of increasing data density (number of measures per child) on the magnitude of RTM of unconditional estimates, and the correlation and concordance between the CRS and four alternative metrics. Further, we demonstrated the differential effect of the choice of velocity metric on the magnitude of the association between infant growth and stunting at 2 years. RESULTS RTM was minimally attenuated by increasing data density for unconditional growth modeling approaches. CRS and classical conditional models gave nearly identical estimates with two measures per child. Compared to the CRS estimates, unconditional metrics had moderate correlation ( r = 0.65–0.91), but poor agreement in the classification of infants with relatively slow growth (kappa = 0.38–0.78). Estimates of the velocity‐stunting association were the same for CRS and classical conditional models but differed substantially between conditional versus unconditional metrics. CONCLUSION The CRS can leverage the flexibility of linear mixed models while addressing RTM in longitudinal analyses.