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The structural network generated from DTI data can be affected by noise in the scans and the choice of tractography algorithm.
Furthermore, even studies with pre- and post-treatment data can be affected by ecological time lags; the longevity of some species may mean that it could take several years to observe a treatment effect.
The interpretability of microarray data can be affected by sample quality.
In addition, the use of administrative data can be affected by surveillance and diagnostic biases.
Validity of EM data can be affected because of discrepancies in pill removal and actual ingestion time.
Last, morbidity data can be affected by the method used to distinguish between the new cases from the follow-ups.
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Since microarray data analysis can be affected by a number of bias [40], we put a particular care on study design and analysed data from the 48 normalized microarrays using multi-way analysis of variance (ANOVA) [41].
Recent research [23, 24] has proven that the performance of data forwarding can be affected gravely when nodes behave selfishly.
As microarray data quality can be affected at each step of the microarray experiment processing (Schuchhardt et al., 2000), quality assessment is an integral part of the analysis.
The analysis demonstrates how data quality can be affected during the diagnostic procedures, as interaction occurs between diagnostics and decisions about medical treatments.
Most of the studies that have been conducted so far have relied on 'convenience samples', which were mainly composed of those who were directly affected, such as victims and residents; were based on non-epidemiological study designs; and used group-level or retrospective, self-reported exposure data, which can be affected by recall and reporting bias [ 71- 74].
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