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Background adjustment, variance stabilization transformation, and normalization (VST-RSN) were carried out using identical parameters for each cohort.
Illumina microarray data were preprocessed, including background adjustment, variance stabilization transformation and quantile normalization as implemented in the lumi package [ 23].
Background adjustment, variance stabilization transformation, robust spline normalization, and the application of detection thresholds were performed identically to the discovery cohort.
Background adjustment, variance stabilization transformation (Lin et al., 2008) and robust spline normalization were performed using the lumi package (Du et al., 2008) in R (R Development Core Team, 2009).
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For example, it would seem reasonable to differentiate between instances of truly inappropriate risk adjustment where variance is due to poor practices (e.g., adjusted for benzodiazepine use for a falls indicator) and "over adjustment" due to the excessive use of spurious risk adjusters resulting in suppression of variance.
The multivariable analysis employed Poisson regression with robust adjustment of variance with presence of diarrhea during the previous week as the outcome variable.
Crude and adjusted prevalence ratios (PR) with their respective 95% confidence intervals (95%CI) were calculated by Poisson regression with robust adjustment of variance since the prevalence of PNNSH was more than 10% in both cities [ 25].
The Poisson regression model with robust adjustment of variance was used to investigate the associations between independent variables and outcomes because the prevalence rates of violence detected were higher than 10%.
Poisson regression was used for multivariate analysis with adjustment for variances (significance of 5%).
Poisson's regression was used for multivariate analysis with adjustment for variances.
Adjustments to variances were carried out using standard multiple imputation approaches.
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