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<h1>Publikation Lieb</h1>
<p>Int. J. Environ. Res. Public Health2021, 18, 9171 4of 15 pressure pain on the radial side of the wrist along with Finkelstein’s test for De Quervain’s tenosynovitis, lateral epicondyle pain, Maudsley’s test for lateral epicondylitis, and the combination of Hoffman–Tinel sign and static two-point discrimination (2-PD) for finger sensibility for nerve entrapment syndromes, adding Phalen’s test specifically for Carpal tunnel syndrome [26–30]. The presence of any of the above diagnoses in a study participant was considered to be a WMSDs diagnosis, which served as the gold standard for correlation analyses with other clinical endpoints and predictors. 2.4. Statistical Analysis The study sample size was initially calculated to provide a sufficient power for the proof that occupational groups did not differ too much (hypothesis of clinical equivalence) in regard to the DASH score [17]. This hypothesis could be confirmed by primary analysis. In the current analysis, correlations between variables were assessed using Spearman correlation coefficients and their p-values. Prior to the calculation of correlation coefficients, bivariate scatter plots were visually examined in order to investigate interrelationships between the clinical endpoints. Prediction ellipses were applied to the scatter plots. Because the ellipse is centered at the two-dimensional mean and expanded to cover the maximal part of the data points, it can visually indicate the strength of interrelation as well as outliers in the data. A stretched tilted ellipse indicates highly correlated variables, whereas an ellipse that is nearly circular indicates little correlation. Statistically, the confidence ellipse visualizes the Pearson correlation coefficient, which is the parametric counterpart of the Spearman coefficient. It seems to be a good first step to show the data distribution and the linearity-based correlation strength and direction. The confidence ellipse works perfectly when the correlation between two variables is linear, but is also applicable to dichotomous variables. Deviations from linearity, outliers, or even the impossibility to, a priory, determine whether the linearity assumption is met, make the Spearman correlation coefficient more reliable in explorative settings. Scatter Plot Matrices with prediction ellipses were used in order to simultaneously visualize bivariate distributions and Pearson correlations in sets of variables. A set of potential predictors influencing clinical endpoints (i.e., independent variables or effects) was chosen based on the clinical considerations, previous studies, and literature data [9]. Six variables were selected for this role: • Gender (female/male) • Body mass index (BMI) • Occupation group (grinding/packaging/control) • Secondary occupation and/or physical hobbies • Age • Years in service A multiple regression modeling of clinical endpoints was applied in order to identify their significant predictors, based on the p-value of the effect. Linear or logistic regression was used, dependent on a respective continuous or dichotomous type of the endpoint variable. Some endpoints in our selection enclose repeated measurements in the same subject, such as left- and right-hand measurements of hand force or range of motion. Additionally, the repetition scheme may have included three subsequent attempts with each arm (hand force) or a recording of three dimensions (E/F, S/P, U/R) for range of motion. Adequate use of such repeated data structures required consideration of more sophisticated repeated measurement modeling methods, taking into account measurements that belong to the same participant when analyzing variance. Repeated measurements methods are superior to just using the mean of the three attempts, as it avoids loss of information and statistical power. Considering handedness (dominant hand) was more challenging than it appears. The study population included four types, as shown in Table 1.</p>
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