Conditional random slope: A new approach for estimating individual child growth velocity in epidemiological research. Issue 5 (21st April 2017)

• Record Type: Journal Article
• Authors: Leung, Michael; Bassani, Diego G.; Racine‐Poon, Amy; Goldenberg, Anna; Ali, Syed Asad; Kang, Gagandeep; Premkumar, Prasanna S.; Roth, Daniel E.
• Is Part Of: American journal of human biology. Volume 29:Issue 5(2017:Sep./Oct.)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-04-21
• Subjects: Human biology -- Periodicals; Physical anthropology -- Periodicals; Biologie humaine -- Périodiques; Anthropologie physique -- Périodiques; 612
• DOI: 10.1002/ajhb.23009
• Languages: English
• ISSNs: 1042-0533

Abstract

Abstract: 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.