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Appropriate parametric and non-parametric tests of comparison were used for univariate testing of LOS by the various factors.
Recently, McDavid et al. (2013) have systematically addressed these issues by proposing a customized approach for univariate testing of differential gene expression of single-cell qPCR data that explicitly takes the component of non-detected qPCR reaction into account.
We will employ Pearson chi-square and/or Fisher's exact tests (for univariate testing) followed by multivariate logistic regression models to identify clinician and patient clinical, demographic, as well as psychosocial characteristics associated with having discussed key topics.
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Thus, we only report the fisher product test [49] for univariate tests.
ANOVA was used for univariate tests, followed by a multivariate regression analysis.
ANOVA was used thereafter for univariate tests between the independent variables and the items in the RIPLS, and mean and standard deviations were also calculated.
Statistical analyses were performed in SPSS, employing Fisher's exact test for categorical variables, either t-test or Mann–Whitney U-test for continuous variables and Spearman correlation analysis for univariate tests.
33 Most of the studies considered a P<0.05 to be significant, though three used P<0.01. 31 40 41 Yates et al used P<0.001 as significant for univariate tests but did not state the significance level for multivariate tests.
Chi-square tests were performed for univariate significance testing, accepting a p value of ≤ 0.05 as statistically significant.
Due to small sample population for each platform we used a random variance model for the univariate tests when distribution assumptions underlying this model were met [41].
Parametrical tests were used for normally distributed data whereas non-parametric tests were applied for all univariate tests to ensure a uniform analysis strategy.
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