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This approach confounds variation due to dissection and RNA preparation with variation between mice.
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In the second approach, confounding is modeled without reference to subject-type in the spirit of the Roy model.
With this approach, confounding variables introduced by disease and medication are avoided.
With this approach, confounding parameters due to unbalanced competencies in the teams can only hardly be controlled.
In humans, existing estimates of these quantities are based on comparisons between consanguineous and nonconsanguineous couples, an approach that confounds socioeconomic and genetic effects of inbreeding.
Much attention has been drawn to the problems that might, in theory, bedevil this approach, including confounding from population structure, misclassification of outcome, and allelic heterogeneity.
Laprise et al. [3] conducted a case control study to estimate the extent to which high levels of periodontal disease were associated with oral cancer risk, using a comprehensive adjustment approach for confounding that involved a large set of life course variables.
This approach reduces confounding by participant characteristics that remain constant over time, while essentially eliminating the risk of selecting unrepresentative controls [21].
The genetic approach avoids confounding and reverse causation effects present in traditional epidemiological studies.
Third, this approach may confound dual use with the proclivity for hospitalization.
In other words, this approach is confounded by pedigree relationships and the non-random sampling of individuals from families at different time points.
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