Table 1 Study characteristics of multivariable prediction models for cognitive outcomes in childhood.

From: Big data, machine learning, and population health: predicting cognitive outcomes in childhood

Authors	Camargo-Figuera et al.²⁰	Camacho et al.²¹	Eriksen et al.²²
Journal	BMC Pediatrics	Paediatric Perinatal Epidemiology	PLoS One
Continent	South America	Europe	Europe
Sample	Pelotas Birth Cohort	Millennium Cohort Study	Lifestyle During Pregnancy Study (sampled from the Danish National Birth Cohort)
Design	Prospective cohort	Prospective cohort	Prospective cohort
Sample size	3312	9487	1782
Year of recruitment	2004	2000–2002	1997–2003^a
Exclusion criteria	Conditions associated with very low IQ, e.g., severe mental retardation	Nil	Multiple pregnancies, language barrier, impaired hearing or vision, congenital disabilities implying mental retardation
Age at cognitive assessment	6	3	5
Cognitive assessment	Wechsler Primary and Preschool Scales of Intelligence -III	Bracken School Readiness Assessment	Wechsler Primary and Preschool Scales of Intelligence – Revised
Cognitive outcome variable	Binary	Binary	Continuous
Cognitive outcome variable	Low IQ defined by a z-score <−1	Not school ready defined by score <1 standard deviation below mean	Continuous
Number of risk factors at outset	32	29	27
Rationale given for candidate variables	Yes—selected based on previous literature and availability	Yes—selected based on previous literature and availability	No–but broad range (>20) selected
Statistical model	Multivariable logistic regression	Multivariable logistic regression	Multivariable linear regression
Method of initial screening of candidate variables	Forward and backward stepwise selection	Forward and backward stepwise selection	Univariable association p ≥ 0.10
Interaction terms fitted	No	No	No
Multicollinearity addressed/discussed	No	Yes	Yes
No. predictors in final model	13	13	9
Validation performed	Yes	Yes	No
Validation performed	Internal and external validation performed	Internal validation only	No
Predictive value measured	External validation	Internal validation	R squared 0.29
	Area under receiver operating curve (AUROC) 0.75	AUROC 0.80
	Sensitivity 70.3%	Sensitivity 72%
	Specificity 68%	Specificity 74%
Predictors in final model	Child—gender, height-for-age deficit; head circumference-for-age deficit	Child—gender, ethnicity, developmental milestones	Child—gender, birth weight, height, head circumference
	Parental—breastfeeding, parental smoking, maternal perception of child’s health, skin colour	Parental—maternal age, maternal mental health, breastfeeding	Parental—maternal BMI, breastfeeding
	Socioenvironmental—parental employment status, maternal education, income, number of siblings, number of persons per room	Socioenvironmental—socioeconomic class, maternal education, income, number of children, employment status, housing type	Socioenvironmental—maternal IQ, parental education

^aIn 2003, a prospective follow-up of 1750 mother–child pairs was initiated, sampled on the basis of maternal alcohol drinking patterns from The Danish National Birth Cohort (DNBC).

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Table 1 Study characteristics of multivariable prediction models for cognitive outcomes in childhood.

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