Strategies for Imputation of High-Resolution Environmental Data in Clinical Randomized Controlled Trials.

Kim, Y; Kelly, S; Krishnan, D; Falletta, J; Wilmot, K

Strategies for Imputation of High-Resolution Environmental Data in Clinical Randomized Controlled Trials.

Kim, Y

Kelly, S

Krishnan, D Falletta, J Wilmot, K

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Int J Environ Res Public Health, 2022, 19, (3)
Issue Date:: 2022-01-24

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Field	Value	Language
dc.contributor.author	Kim, Y https://orcid.org/0000-0002-2829-1212
dc.contributor.author	Kelly, S https://orcid.org/0000-0002-8454-6144
dc.contributor.author	Krishnan, D
dc.contributor.author	Falletta, J
dc.contributor.author	Wilmot, K https://orcid.org/0000-0001-8996-5789
dc.date.accessioned	2022-03-28T06:08:22Z
dc.date.available	2022-01-19
dc.date.available	2022-03-28T06:08:22Z
dc.date.issued	2022-01-24
dc.identifier.citation	Int J Environ Res Public Health, 2022, 19, (3)
dc.identifier.issn	1661-7827
dc.identifier.issn	1660-4601
dc.identifier.uri	http://hdl.handle.net/10453/155640
dc.description.abstract	Time series data collected in clinical trials can have varying degrees of missingness, adding challenges during statistical analyses. An additional layer of complexity is introduced for missing data in randomized controlled trials (RCT), where researchers must remain blinded between intervention and control groups. Such restriction severely limits the applicability of conventional imputation methods that would utilize other participants' data for improved performance. This paper explores and compares various methods to impute high-resolution temperature logger data in RCT settings. In addition to the conventional non-parametric approaches, we propose a spline regression (SR) approach that captures the dynamics of indoor temperature by time of day that is unique to each participant. We investigate how the inclusion of external temperature and energy use can improve the model performance. Results show that SR imputation results in 16% smaller root mean squared error (RMSE) compared to conventional imputation methods, with the gap widening to 22% when more than half of data is missing. The SR method is particularly useful in cases where missingness occurs simultaneously for multiple participants, such as concurrent battery failures. We demonstrate how proper modelling of periodic dynamics can lead to significantly improved imputation performance, even with limited data.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Int J Environ Res Public Health
dc.relation.isbasedon	10.3390/ijerph19031307
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Toxicology
dc.subject.mesh	Humans
dc.subject.mesh	Randomized Controlled Trials as Topic
dc.subject.mesh	Research Design
dc.subject.mesh	Time Factors
dc.subject.mesh	Humans
dc.subject.mesh	Research Design
dc.subject.mesh	Time Factors
dc.subject.mesh	Randomized Controlled Trials as Topic
dc.title	Strategies for Imputation of High-Resolution Environmental Data in Clinical Randomized Controlled Trials.
dc.type	Journal Article
utslib.citation.volume	19
utslib.location.activity	Switzerland
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
pubs.organisational-group	/University of Technology Sydney/Strength - ISF - Institute for Sustainable Futures
utslib.copyright.status	open_access	*
dc.date.updated	2022-03-28T06:08:17Z
pubs.issue	3
pubs.publication-status	Published online
pubs.volume	19
utslib.citation.issue	3

Abstract:

Time series data collected in clinical trials can have varying degrees of missingness, adding challenges during statistical analyses. An additional layer of complexity is introduced for missing data in randomized controlled trials (RCT), where researchers must remain blinded between intervention and control groups. Such restriction severely limits the applicability of conventional imputation methods that would utilize other participants' data for improved performance. This paper explores and compares various methods to impute high-resolution temperature logger data in RCT settings. In addition to the conventional non-parametric approaches, we propose a spline regression (SR) approach that captures the dynamics of indoor temperature by time of day that is unique to each participant. We investigate how the inclusion of external temperature and energy use can improve the model performance. Results show that SR imputation results in 16% smaller root mean squared error (RMSE) compared to conventional imputation methods, with the gap widening to 22% when more than half of data is missing. The SR method is particularly useful in cases where missingness occurs simultaneously for multiple participants, such as concurrent battery failures. We demonstrate how proper modelling of periodic dynamics can lead to significantly improved imputation performance, even with limited data.

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http://hdl.handle.net/10453/155640