This repository contains code for our recommended algorithm (NLME-A), capable of data cleaning growth measurements in a broad spectrum of domains. It also contains code for several other data cleaning methods for growth and compares their effectiveness to our method by simulating errors in a publicly available pre-cleaned human weight measurement dataset known as 'CLOSER', obtained from the UK data archive1.
The code contained in this repository is supplementary material for our publication2, which provides further details about the methodologies used.
References
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Cohort and Longitudinal Studies Enhancement Resources. Harmonised Height, Weight and BMI in Five Longitudinal Cohort Studies: National Child Development Study, 1970 British Cohort Study and Millennium Cohort Study. [data collection]. UK Data Service, 2017 [Accessed 6 December 2018]. Available from: http://doi.org/10.5255/UKDA-SN-8207-1
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Woolley CSC, Handel IG, Bronsvoort BM, Schoenebeck JJ, Clements DN (2020) Is it time to stop sweeping data cleaning under the carpet? A novel algorithm for outlier management in growth data. PLoS ONE 15(1): e0228154. https://doi.org/10.1371/journal.pone.0228154