Pooling individual participant data from randomized controlled trials: Exploring potential loss of information

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dc.creatorvan Wanrooij, Lennard L.
dc.creatorHoevenaar-Blom, Marieke P.
dc.creatorColey, Nicola
dc.creatorNgandu, Tiia
dc.creatorMeiller, Yannick
dc.creatorGuillemont, Juliette
dc.creatorRosenberg, Anna
dc.creatorBeishuizen, Cathrien R. L.
dc.creatorMoll van Charante, Eric P.
dc.creatorSoininen, Hilkka
dc.creatorBrayne, Carol
dc.creatorAndrieu, Sandrine
dc.creatorKivipelto, Miia
dc.creatorRichard, Edo
dc.date2020-05-12T22:03:46Z
dc.date2020-05-12T22:03:46Z
dc.date2020-05-12
dc.date2019-11-28
dc.date2020-05-12T22:03:46Z
dc.date.accessioned2026-08-03T01:40:47Z
dc.descriptionBackground: Pooling individual participant data to enable pooled analyses is often complicated by diversity in variables across available datasets. Therefore, recoding original variables is often necessary to build a pooled dataset. We aimed to quantify how much information is lost in this process and to what extent this jeopardizes validity of analyses results. Methods: Data were derived from a platform that was developed to pool data from three randomized controlled trials on the effect of treatment of cardiovascular risk factors on cognitive decline or dementia. We quantified loss of information using the R-squared of linear regression models with pooled variables as a function of their original variable(s). In case the R-squared was below 0.8, we additionally explored the potential impact of loss of information for future analyses. We did this second step by comparing whether the Beta coefficient of the predictor differed more than 10% when adding original or recoded variables as a confounder in a linear regression model. In a simulation we randomly sampled numbers, recoded those < = 1000 to 0 and those >1000 to 1 and varied the range of the continuous variable, the ratio of recoded zeroes to recoded ones, or both, and again extracted the R-squared from linear models to quantify information loss. Results: The R-squared was below 0.8 for 8 out of 91 recoded variables. In 4 cases this had a substantial impact on the regression models, particularly when a continuous variable was recoded into a discrete variable. Our simulation showed that the least information is lost when the ratio of recoded zeroes to ones is 1:1. Conclusions: Large, pooled datasets provide great opportunities, justifying the efforts for data harmonization. Still, caution is warranted when using recoded variables which variance is explained limitedly by their original variables as this may jeopardize the validity of study results.
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dc.identifierpone-d-19-33001
dc.identifierhttps://www.repository.cam.ac.uk/handle/1810/305299
dc.identifier10.17863/CAM.52384
dc.identifier1932-6203
dc.identifier.urihttps://repo.dare.co.zw/handle/123456789/157743
dc.languageen
dc.publisherPublic Library of Science
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.subjectResearch Article
dc.subjectResearch and analysis methods
dc.subjectPhysical sciences
dc.subjectBiology and life sciences
dc.subjectMedicine and health sciences
dc.titlePooling individual participant data from randomized controlled trials: Exploring potential loss of information
dc.typeArticle

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