Spss v 25 windows

We expressed data as mean ± standard deviation or percentage. We presented categorical variables as numbers (percentages) and compared them using the Chi-square test. For continuous variables, we performed the Shapiro-Wilk test to confirm whether the distribution of continuous variables was normal or not. We compared continuous variables with normal distribution using a student t-test. Otherwise, we compared continuous variables using a Mann-Whitney U test. We constructed event-free survival curves using the Kaplan−Meier method. The log-lank test was used to assess statistical differences between curves. We also carried out a multivariate Cox hazard analysis to examine the association between poor appetite and MACE after controlling for confounding factors. Variables that were significantly different (p < 0.05) between the good appetite group and the poor appetite group were considered confounding factors. We did not include variables with ≥5 missing values in the model. Similar variables were not included in the model simultaneously to avoid multicollinearity. We calculated hazard ratios and the 95% confidence intervals (CI). We considered p value < 0.05 statistically significant. We performed all analyses using statistical software, SPSS v. 25/Windows (SPSS, Chicago, IL, USA).

We expressed data as mean ± standard deviation or percentage. We presented categorical variables as numbers (percentages) and compared them using the Chi-square test. For continuous variables, we performed the Shapiro-Wilk test to confirm whether the distribution of continuous variables was normal or not. We compared continuous variables with normal distribution using a student t-test. Otherwise, we compared continuous variables using a Mann-Whitney U test. We constructed event-free survival curves using the Kaplan−Meier method. The log-lank test was used to assess statistical differences between curves. We also carried out a multivariate Cox hazard analysis to examine the association between poor appetite and MACE after controlling for confounding factors. Variables that were significantly different (p < 0.05) between the good appetite group and the poor appetite group were considered confounding factors. We did not include variables with ≥5 missing values in the model. Similar variables were not included in the model simultaneously to avoid multicollinearity. We calculated hazard ratios and the 95% confidence intervals (CI). We considered p value < 0.05 statistically significant. We performed all analyses using statistical software, SPSS v. 25/Windows (SPSS, Chicago, IL, USA).