Background: Participants' 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.
Methods: In 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.
Results: 5516 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.
Conclusions: Identification 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.