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                <identifier>oai:data.mendeley.com/mj8zh92gwf.2</identifier>
                <datestamp>2026-06-23T20:31:36Z</datestamp>
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    <dc:creator>Boakye, Nancy Fosua</dc:creator>
    <dc:title>Codes for &quot;Predicting Childhood Obesity Risk: A Longitudinal Study of Children in Ireland&quot;</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>These are the codes for the modelling process used in Predicting Childhood Obesity Risk: A Longitudinal Study of Children in Ireland.  

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.

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.


</dc:description>
    <dc:subject>Obesity</dc:subject>
    <dc:subject>Machine Learning</dc:subject>
    <dc:subject>Multivariate Logistical Regression</dc:subject>
    <dc:contributor>Jalali, Amirhossein</dc:contributor>
    <dc:contributor>Hannigan, Ailish</dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/mj8zh92gwf.2</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/mj8zh92gwf.2</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/mj8zh92gwf</dc:relation>
    <dc:date>2026-06-23T20:31:36Z</dc:date>
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