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Data · dataset · 2026

Codes for "Predicting Childhood Obesity Risk: A Longitudinal Study of Children in Ireland"

Listed in Teesside University Research Data Repository

These are the codes for the modelling process used in Predicting Childhood Obesity Risk: A Longitudinal Study of Children in Ireland.

Description

Researchers wishing to reproduce our results may apply for access to the GUI dataset through the Irish Social Science Data Archive (ISSDA) at www.ucd.ie/issda and to the MCS dataset through the UK Data Service at www.ukdataservice.ac.uk. Model Development ( Apparent and Internal Validation) Step 1: We pooled the candidate variables from the Growing Up in Ireland (GUI) study database.

Step 2: RecodingCandidateVariables.R ----- Recode, create composite variables in the candidate variables in the GUI data Step 3: MICE_Strategies.R ----- Remove overlapping variables following expert knowledge and statistics strategies in the GUI dataset. Step 4: sample_size_calculation.R ----- Calculate the minimum required sample size for the GUI dataset. Step 5: MICE_GUI.R----- Multiple impute missing values and performed weighted lasso for variable selection in the GUI data Step 6: Development_process.R----- Develop and internally validate models based on AUC, calibration and decision curve analysis.

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External Validation Step 7: MCS_recoding.R ----- Recode external validation dataset to have the same format as the development dataset. Step 8: MICE_MCS.R ----- External validation sample size calculation and multiple imputation by MICE. Step 9: External_validation.R ----- Externally validate the developed models.

Additional files referenced in the manuscript are as follows: Additional file 1: Detailed variable descriptions across the growing up in Ireland and millennium cohort study datasets. Additional file 2: A comparison between participants missing versus non-missing outcome data across the growing up in Ireland and millennium cohort study datasets. Additional file 3: A summary statistics for all included variables for non-obese and obese group for the growing up in Ireland dataset.

Additional file 4: A summary statistics for all included variables for non-obese and obese group for millennium cohort study dataset. Additional file 5: A summary CSV file detailing the selection frequency of each predictor variable across 40 imputed datasets using various the modelling approaches. Additional file 6: Distribution of the predicted risks by outcome in millennium cohort study dataset.

Additional file 7: The TRIPOD+AI checklist.

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From keywords
Computer Science & AI · Earth & Environmental Science · Machine learning
Inferred from text
Longitudinal study 75%
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