Abstract
Measurement errors commonly occur in 24-h hormonal data and may affect the outcomes of such studies. Measurement errors often appear as outliers in such data sets; however, no well-established method is available for their automatic detection. In this study, we aimed to compare performances of different methods for outlier detection in hormonal serial data. Hormones (glucose, insulin, thyroid-stimulating hormone, cortisol, and growth hormone) were measured in blood sampled every 10 min for 24 h in 38 participants of the Leiden Longevity Study. Four methods for detecting outliers were compared: (1) eyeballing, (2) Tukey’s fences, (3) stepwise approach, and (4) the expectation-maximization (EM) algorithm. Eyeballing detects outliers based on experts’ knowledge, and the stepwise approach incorporates physiological knowledge with a statistical algorithm. Tukey’s fences and the EM algorithm are data-driven methods, using interquartile range and a mathematical algorithm to identify the underlying distribution, respectively. The performance of the methods was evaluated based on the number of outliers detected and the change in statistical outcomes after removing detected outliers. Eyeballing resulted in the lowest number of outliers detected (1.0% of all data points), followed by Tukey’s fences (2.3%), the stepwise approach (2.7%), and the EM algorithm (11.0%). In all methods, the mean hormone levels did not change materially after removing outliers. However, their minima were affected by outlier removal. Although removing outliers affected the correlation between glucose and insulin on the individual level, when averaged over all participants, none of the 4 methods influenced the correlation. Based on our results, the EM algorithm is not recommended given the high number of outliers detected, even where data points are physiologically plausible. Since Tukey’s fences is not suitable for all types of data and eyeballing is time-consuming, we recommend the stepwise approach for outlier detection, which combines physiological knowledge and an automated process.
Highlights
Many physiological parameters such as hormones or metabolites exhibit rhythmicity
The sleep-wake cycle is another form of rhythm, and similar to the circadian rhythm, it has other effects on hormone secretion than the biological clock
Cross-correlation estimates the temporal relationship between 2 hormonal concentrations. It is a common analysis performed with data of 2 simultaneously measured hormonal time series (Vis et al, 2014). It could be of interest for researchers to know to which extent measurement error would affect the estimates, especially since this method might be sensitive to the presence of outliers that cooccur in different time series data, for example, due to the dilution of a sample
Summary
Many physiological parameters such as hormones or metabolites exhibit rhythmicity. These rhythms are regulated by different systems. The biological clock synchronizes molecular clocks in peripheral cells and orchestrates many physiological functions including blood pressure, core body temperature, and hormone secretion. An example of a hormone that exhibits a strong circadian rhythmicity is cortisol. The sleep-wake cycle is another form of rhythm, and similar to the circadian rhythm, it has other effects on hormone secretion than the biological clock. The secretion of growth hormone (GH), for example, is more strongly influenced by sleep than by clock time. External cues, including food intake and physical activity, can influence hormone secretion, such as the secretion of insulin (Oike et al, 2014)
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