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Examination of multiple independent outcomes can lead to spurious significant findings, and increase type I errors.
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Agency models of multiple tasks typically assume independent outcomes.
These permit the identification of additive linear effects of multiple independent variables on an outcome, but cannot identify complex causal paths.
To assess outcomes across multiple independent groups, one-way ANOVA was used.
Only after consistent association with clinical outcomes in multiple independent studies should genes be considered for inclusion in an assay used to make clinical decisions.
First, combining predictors of net benefit (risk factors for bad outcomes in the absence of vs. in the presence of the treatment) into a single prediction tool can greatly increase the degree of risk-stratification, since most common health outcomes have multiple independent risk factors [ 22, 28- 40].
Logistic regression analysis is the most frequently used statistical analysis for evaluating multiple independent variables through a dependent outcome in medical research.
Assuming each signature is primarily driven by a major process, somatic mutations would be the outcome of multiple independent biological processes in cancer genomes.
Further refinement of the signature provided us with a concise list of nine genes that hallmark prostate cancer outcome in multiple independent validation series.
This test was subsequently found to differentiate between patients on the basis of postsurgical outcome in multiple independent retrospective cohorts[ 20, 21].
Our integrated genomic strategy allowed us to dissect out a core TGF-β/Smad3 gene signature that specifically reflected the tumor-suppressive activities of TGF-β in vivo and was associated with good outcome in multiple independent ER+ breast cancer cohorts.
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