Table 3.

Statistical analysis

Variables	Details of statistical analysis
Baseline characteristics	Baseline characteristics were compared between the four groups using the chi-square test, variance or Kruskal-Wallis tests when appropriate.
Logistic regression analysis	Multiple-adjusted logistic regression analysis was used to estimate the risk of cognitive impairment by calculating odds ratio (OR) and 95% confidence interval (CI). Model 1 adjusted for age, sex, baseline National Institutes of Health Stroke Scale scores, education, current smoking, alcohol drinking, systolic blood pressure, estimated glomerular filtration rate, body mass index, time from onset to randomization, ischemic stroke subtype, and family history of stroke. Model 2 included the factors in model 1 as well as the use of antihypertensive treatment and hypoglycemic treatment. Model 3 included the factors in model 2 as well as medical history of hypertension, medical history of diabetes mellitus, medical history of hyperlipidemia, and medical history of coronary heart disease. Potential covariates for cognitive impairment were selected based on prior knowledge.
Effect modification by renal function	We tested the statistical significance of cystatin C quartiles × renal function status on the cognitive impairment in multivariable logistic model by the likelihood ratio test. We further evaluated the pattern and magnitude of associations between serum cystatin C and cognitive impairment using a logistic regression model with restricted cubic splines among the patients without or with normal renal function, with four knots (at the 5th, 35th, 65th, and 95th percentiles).
Statistical software	Statistical analysis was conducted using SAS statistical software (version 9.4, Cary, NC, USA).

All P values were two-tailed, and a significance level of 0.05 was used.