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The false evidence rate: An approach to frequentist error rate control conditioning on the observed value.
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A value is conventionally interpreted either as a) the probability by chance of obtaining more extreme results than those observed or b) a tool for declaring significance at a prespecified level. Both approaches carry difficulties: b) does not allow users to make inferences based on the data in hand, and is not rigorously followed by researchers in practice, while (a) is not meaningful as an error rate. Although values retain an important role, these shortcomings are likely to have contributed significantly to the scientific reproducibility crisis.
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Genome-wide association studies (GWAS) have detected several susceptibility variants for urinary bladder cancer, but how gene regulation affects disease development remains unclear. To extend GWAS findings, we conducted a transcriptome-wide association study (TWAS) using PrediXcan to predict gene expression levels in whole blood using genome-wide genotype data for 6180 bladder cancer cases and 5699 controls included in the database of Genotypes and Phenotypes (dbGaP). Logistic regression was used to estimate adjusted gene-level odds ratios (OR) per 1-standard deviation higher expression with 95% confidence intervals (CI) for bladder cancer risk.
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Center for Translational Immunology, University Medical Center Utrecht, KC 02.085.2, P.O. Box 85090, 3508 AB, Utrecht, The Netherlands.
The proximity extension assay (PEA) enables large-scale proteomic investigations across numerous proteins and samples. However, discrepancies between measurements, known as batch-effects, potentially skew downstream statistical analyses and increase the risks of false discoveries. While implementing bridging controls (BCs) on each plate has been proposed to mitigate these effects, a clear method for utilizing this strategy remains elusive.
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