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The improved statistical power of the multivariable approach is merely a by-product of improved risk-stratification. Since a multi-variable model stratifies patients into subgroups with a much wider range of pre-treatment risks, there is more variation in net RRR across treatment groups, and hence, it is easier to detect the underlying heterogeneity in net treatment benefit.
In the GWAS case study on four cholesterol phenotypes and serum metabolites, the MLT method improved statistical power by 5% to 38% depending on the number and effect sizes of the conditional SNPs.
This can deliver improved statistical power [ 13].
Such integrative approaches have improved statistical power [ 56, 57].
The authors also demonstrated improved statistical power with this method.
Robustness and improved statistical power can be achieved through intensity-level integration of microarray data [ 13].
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Females were oversampled to improve statistical power for sex-specific analyses.
However, translating a reduction in COV to improve statistical power requires additional assumptions, e.g., regarding the potential treatment effect [50].
Some studies have combined patients with different pathological diagnoses to improve statistical power however it is highly likely that the dose response will be pathology dependent [13].
To improve statistical power, we considered men and women together.
This analysis method avoids multiple testing issues and improves statistical power compared to the conventional approach.
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