EMPIRICAL MODELS FOR LONGITUDINAL POST-NATAL GROWTH
SuperLearning to define and predict composite outcomes
To develop a data-driven method for combining multivariate outcome measures (e.g., achievement scores) into a composite score in situations where investigators lack scientific grounding for the use of other composite scores.
0-2 year (anthropometry); 11 years (test scores)
Native language, learned language, and math competency test scores.
Predictors (other than time)
Health care access; use of preventive health care; child:adult ratio; child dependency ratio; crowding index; urban score; total family income; socioeconomic status; sanitation; access to clean water; mother's age,height, years of education, marital status, age at first child, and parity; father's age and years of education; weight-for-age z-score and HAZ (0,6,12,18, and 24 months); maternal smoking during pregnancy, child's sex and gestational age at birth
Do we have a tool for visualizing model output/predictions? If not, do we plan to?