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This highlights the importance of standardizing the preanalytic variables affecting results when utilizing miRNAs as noninvasive biomarkers.
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Recent evidence reinforces how technology used to measure acid base variables affects results and may affect interpretation of clinical studies.
The degree to which the epidemiologic results differ from these predictions likely indicates the degree to which confounding variables are affecting results.
The goal of sensitivity analysis (SA) is to study this relationship and identify the most significant factors or variables affecting the results of the model.
By using a tightly controlled study design, the probability of noncompliance and confounding variables affecting these results is much lower than in studies with free-living participants.
Sensitivity toolbox allows to study the relationships between the input and output factors, and to identify the most significant variables affecting the results of the model [31,35].
Indeed, the K-means cluster analysis is exploratory in nature, and the scale and variability of the clustering variables may affect results in unstandardised analyses [ 74].
Only recently, however, have rigorous attempts been made to standardize all the relevant variables that affect results of the SBT.
Differences in sample collection dates, geographic location, and other variables could affect results, although no evidence of any confounding effect was detected.
Besides domain specificity, different cognitive variables affect the results of problem-solving processes (Kröner et al. 2005).
The aim of the study presented in this paper was to investigate the extent to which the assumption of cross-national measurement invariance of latent background variables affected the results of prediction models that use these indices as predictors in large-scale assessment studies.
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