EMPIRICAL MODELS FOR LONGITUDINAL POST-NATAL GROWTH

SuperLearning of child growth trajectories

To describe variability in longitudinal grwoth trajectories.

AGE RANGE

Study specific, currently uses all ages used in a particular study. However, model training can be also restricted to a particular age range.

OUTCOMES

Most work with HAZ, but some with HTCM. General approach can be applied to any longitudinally measured anthropometric measure.

Predictors (other than time)

Predictors used were study specific: all baseline and time varying covariates available in the study were typically included, unless the predictor was known to be highly correlated with the outcome of interest. In addition, the following summaries of observed child-specific growth measurements were used as predictors: total number of measurements available and mean / median / SD / min / max of available growth measurements.

Do we have a tool for visualizing model output/predictions? If not, do we plan to?

No data available

Status

On-going

On-going work

Development of an R package for public use.

Documentation / Code / Slides / Tools

No data available

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