ArticlePloS one2015
Detection of Outliers Due to Participants' Non-Adherence to Protocol in a Longitudinal Study of Cognitive Decline.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The effect of varenicline on mood and cognition in smokers with HIV.Psychopharmacology · 2020Trial
- Cognitive impairment is associated with poorer oral health in people with HIV: evidence from a pilot study.Quintessence international (Berlin, Germany : 1985) · 2026Article
- Hidden risk: Latent cognitive profiles and structural brain age reveal vulnerability in midlife metabolic syndrome.Journal of the International Neuropsychological Society : JINS · 2025Article
- Health literacy in relation to web-based measurement of cognitive function in the home: UK Women's Cohort Study.BMJ open · 2025Article
- Detecting potential outliers in longitudinal data with time-dependent covariates.European journal of clinical nutrition · 2024Article
- Does pattern mixture modelling reduce bias due to informative attrition compared to fitting a mixed effects model to the available cases or data imputed using multiple imputation?: a simulation study.BMC medical research methodology · 2018Article
- High quality standards for a large-scale prospective population-based observational cohort: Constances.BMC public health · 2016Article
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6 authors.
Funding
Abstract
backgroundParticipants' non adherence to protocol affects data quality. In longitudinal studies, this leads to outliers that can be present at the level of the population or the individual. The purpose of the present study is to elaborate a method for detection of outliers in a study of cognitive ageing.
methodsIn the Whitehall II study, data on a cognitive test battery have been collected in 1997-99, 2002-04, 2007-09 and 2012-13. Outliers at the 2012-13 wave were identified using a 4-step procedure: (1) identify cognitive tests with potential non-adherence to protocol, (2) choose a prediction model between a simple model with socio-demographic covariates and one that also includes health behaviours and health measures, (3) define an outlier using a studentized residual, and (4) study the impact of exclusion of outliers by estimating the effect of age and diabetes on cognitive decline.
results5516 participants provided cognitive data in 2012-13. Comparisons of rates of annual decline over the first three and all four waves of data suggested outliers in three of the 5 tests. Mean residuals for the 2012-13 wave were larger for the basic compared to the more complex prediction model (all p<0.001), leading us to use the latter for the identification of outliers. Residuals greater than two standard deviation of residuals identified approximately 7% of observations as being outliers. Removal of these observations from the analyses showed that both age and diabetes had associations with cognitive decline similar to that observed with the first three waves of data; these associations were weaker or absent in non-cleaned data.
conclusionsIdentification of outliers is important as they obscure the effects of known risk factor and introduce bias in the estimates of cognitive decline. We showed that an informed approach, using the range of data collected in a longitudinal study, may be able to identify outliers.
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