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Our purpose was to establish an optimized pipeline to collect biological samples for blood transcriptomics to minimize confounding signals such as artifacts of sample preparation and processing while maximizing reproducibility and sensitivity.
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To minimize confounding factors due to ethnical differences, all patients selected in the study were Caucasians and had European origins.
To minimize confounding by population stratification, our analyses were restricted to the subset of African Americans.
All models were conducted in the overall population and separately by race to minimize confounding by population stratification.
To minimize confounding by changing street environments, case families were asked if environmental modifications were made since the time injury.
In order to minimize confounding bias, treatment-by-covariate interactions were incorporated in the models [ 17].
Using class-wide analyses may minimize confounding because most applicators used multiple pesticides.
The healthy normal group had extensive exclusion criteria to minimize confounding due to age-related conditions.
In addition, methods for data assessment and analysis should aim to minimize confounding variables (see below).
Several precautions were taken to minimize confounding due to learning effects.
This was to minimize confounding by indication, which was a limitation of the previous studies.
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