2.4 Statistical analysis
Categorical variables were expressed as frequencies and percentages
(%), and the frequencies of non-survived, survived severe, and
non-severe patient groups (total patient number = 289) were compared by
partition of chi-square test. Continuous variables were described
as median and interquartile ranges (IQR) values, and one-way ANOVA test
and Kruskal-Wallis test were used, as appropriate, to compare the
data from three groups. A two-sided α of 0.0167 (after
adjustment) was considered statistically significant for partition ofchi-square test when compare differences between categorical
variables among 3 groups. On the other hand, a two-sided α of
0.05 was considered statistically significant for one-way ANOVA test andKruskal-Wallis test when compare differences between continuous
variables among 3 groups.
Principal Component Analysis (PCA) was used for dimensionality reduction
and visualization of the patients after imputing missing values using
the implementation ”ppca” in pca Methods package (cite:
https://academic.oup.com/bioinformatics/article/23/9/1164/272597).
Euclidean distance and complete linkage were used for the heatmap
between different laboratory parameters in three groups. Statistical
analyses and figures were generated and plotted by GraphPad Prism
version 7.00 software (GraphPad Software Inc), SPSS software (version
26.0, IBM) and R software (version 3.4.3, supported by R Foundation for
Statistical Computing).