Statistical analysis
septic shock
EVENT, INFECTION SITES, SEPTIC SHOCK
The primary analysis was based on intention-to-treat. Patients who survived for > 72 h after randomization were included in the per-protocol analysis. Considering the impact of population characteristics on outcomes, we conducted subgroup analysis in populations with septic shock or with different infection sites and bacteria.Statistical analysis was conducted using SAS 9.4. The continuous variable data were subjected to Kolmogorov–Smirnov test to determine whether they adhered to normal distribution. Mean value (SD) or quartile (upper quartile Q1, median Q2, lower quartile Q3) was used to describe the baseline characteristics of continuous variables, and the percentage was used for descriptive analysis of counting variables.The primary endpoints of patients lost to follow-up were deemed ineffective. A log-rank test was used to compare time-to-event endpoints, and patients without an event were censored up to the date last known to be at risk; also, Kaplan–Meier estimation was carried out. A chi-square test was used to examine the binary endpoints. For the risk difference and relative risk, Wald and Newcombe confidence intervals were offered, respectively. Continuous endpoints were analyzed with a two-independent sample t test or the Wilcoxon rank test. All the reported Based on the primary outcomes, we calculated a sample size of 311 that could achieve 80% power to detect a relative risk of 1.260 at a significance level of 0.05, and the 14-day clinical response rate in the low-dose group was assumed to be 58.0%.