2.4 Statistical analysis
Demographics and baseline characteristics of subjects were presented as
mean ± standard deviation for continuous data with normal distribution
and median (IQR) for continuous data with skewed variables, and compared
by Student’s t -test and Mann-Whitney U test, respectively.
Categorical data were presented as percentages and compared using the
Chi-square or Fisher’s exact test, as appropriate. Odds ratios (ORs) and
95% confidence intervals (95% CI) were calculated in unadjusted and
multivariate-adjusted logistic regression model analyses. Stepwise
multivariate logistic regression analysis was used to explore the
independent influencing factors of osteoporosis and ED. Least absolute
shrinkage and selection operator (LASSO) logistic regression, which is
suitable for the regression of high-dimensional data, was used to select
important preoperative indices for predicting postoperative hypokalemia.
LASSO logistic regression analysis was performed using the glmnet R
package. Receive operating characteristic (ROC) analysis was performed
to assess the accuracy of the different models for osteoporosis. All
statistical assessments were performed using RStudio statistical
programming language (version 3.1.6). Two-tailed P<0.05 were considered indicative of statistical significance.