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

Autor: van Wanrooij LL; Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands., Hoevenaar-Blom MP; Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.; Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands., Coley N; Department of Epidemiology and Public Health, Toulouse University Hospital, Toulouse, France.; INSERM, University of Toulouse UMR1027, Toulouse, France., Ngandu T; Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland., Meiller Y; Department of Information and Operations Management, ESCP Europe, Paris, France., Guillemont J; INSERM, University of Toulouse, Toulouse, France., Rosenberg A; Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland., Beishuizen CRL; Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands., Moll van Charante EP; Department of General Practice, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands., Soininen H; Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.; Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland., Brayne C; Department of Public Health and Primary Care, Cambridge Institute of Public Health, University of Cambridge, Cambridge, United Kingdom., Andrieu S; Department of Epidemiology and Public Health, Toulouse University Hospital, Toulouse, France.; INSERM, University of Toulouse UMR1027, Toulouse, France., Kivipelto M; Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland.; Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.; Aging Research Center, Karolinska Institutet, Stockholm University, Stockholm, Sweden.; Karolinska Institutet Center for Alzheimer Research, Stockholm, Sweden., Richard E; Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.; Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2020 May 12; Vol. 15 (5), pp. e0232970. Date of Electronic Publication: 2020 May 12 (Print Publication: 2020).
DOI: 10.1371/journal.pone.0232970
Abstrakt: Background: 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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