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Although the study population represents one of the largest samples in the literature, we do not have statistical power to assess the role of patient characteristics in a multivariate analysis.
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The largest sample in the current review had 286 patients (22) and studies commonly reported on samples with a mix of cancer diagnoses, conditioning regimens, and transplant types.
"Everything you do is so dependent on large samples, and you can't get large samples in small places".
Similar samples are expected to be measurable in both systems, however, slightly longer measurement times may be anticipated for large samples in HLNB.
Because people with few cognitive deficits represent a heterogeneous population, prevention studies require large samples in order to detect active drug effects.
The comorbidity of airplane headaches and (mild or severe) psychiatric disorders should be investigated in larger samples in order to establish a significant association.
As Dudoit and van der Laan [8] have shown, the first question is well defined with larger samples in V-fold cross-validation.
The benefit of large samples in improving statistical power and detecting interactions of much smaller effect sizes may be validated in the future.
This characteristic should be tested in larger samples in the future.
Future studies should therefore incorporate larger samples in order to test for possible interactions.
Future studies should include larger samples in different clinical settings and multi-method designs.
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CEO of Professional Science Editing for Scientists @ prosciediting.com