Statistical methods for primary and secondary outcomes {20a}
sleepiness, PVT, behavioral impairment
REGRESSION, SEPARATION, SECONDARY
Based on our study design, statistical analysis will be performed to detect both intra- and inter-individual (i.e., individual and group) differences. All the analyses will be carried out in reference to hours of wakefulness (i.e., clock time), and also in reference to the internal body time (IBT) of the participants which is determined by the dim light melatonin onset (DLMO) from oral fluid samples.The primary outcome of the trial is the changes detected in the metabolome. Concentration values of metabolic features will be used to generate feature time series. As a first characterization, the rhythmicity of features will be tested by cosinor analysis. Other regression models such as linear, exponential, or logarithmic functions will be applied for fitting time series data in order to simplify complex patterns. To extract relevant features from the time series, different statistical approaches will be carried out. Firstly, we will investigate how group-level differences vary when applying generalized linear mixed models and — if group-level differences exist — conduct multiple comparison tests. After applying the corrections for multiple testing (Benjamini–Hochberg procedure, i.e., false discovery rate), adjusted p-values will be considered significant when below 0.05. Significant features will then be shortlisted. Secondly, each time series will be checked for the occurrence of regular patterns (e.g., linear increase/decrease, exponential, etc.) in relation to hours of wakefulness. Again, each feature candidate will be added to the shortlist. Lastly, a classification task will be carried out to isolate those features leading to a separation between the study arms. The result of the primary outcome will be a list of candidate biomarkers.For secondary outcomes, differences between study groups will be reported. For variables that are continuous (i.e., outcome of PVT, d2-task, driving simulator parameters, EEG data) the mean and the median will be computed accordingly. For variables that are either binary or ordinal (i.e., results of the behavioral impairment test, visual attention test, sleepiness scales, expert decision on driving ability), the relative frequencies, proportions, and standard errors for each group will be reported.Regarding the metabolic correlation between blood and non-invasive specimens as a secondary outcome, a regression analysis will be performed between metabolite concentration values in blood and in other sampled matrices.As a last step, causality between all outcomes will be tested by cross-correlation analyses.