2.5 | Statistical analyses
Propensity score matching was adopted to optimize comparability between
insulin users and nonusers [17]. The propensity score was estimated
for every person through nonparsimonious multivariable logistic
regression, with insulin treatment as the dependent variable. We used 26
clinically related variables in the analysis as controlling variables
(Table 1). The nearest-neighbor algorithm was adopted to construct
matching pairs under the assumption that the proportion of 0.995–1.0
was perfect [18].
Crude and multivariate-adjusted Cox proportional hazard models with
robust sandwich standard error estimates were used to compare the risk
of outcomes between insulin users and nonusers. The results are
presented as hazard ratios (HRs) with 95% confidence intervals (CIs)
for insulin users versus nonusers. To assess the risk of all-cause
mortality, we checked the persons’ time of death or the end of the
study, whichever occurred first. For other outcomes, we checked the
persons’ date of respective outcomes or end of follow-up on December 31,
2013, whichever occurred first. We compared the cumulative incidence of
all-cause mortality, MACE, decompensated cirrhosis, and hepatic failure
over time between insulin users and nonusers using the Kaplan–Meier
method and log-rank tests.
We conducted a sensitivity test by excluding persons with hypoglycemia
before or after the index date; matching insulin users and nonusers; and
calculating the incidence and hazard ratio of death, MACE, and
liver-related outcomes to avoid the interference from hypoglycemia on
other main outcomes.
Two-tailed P < .05 was considered as significant. SAS
v9.4 (SAS Institute, Inc., Cary, NC, USA) was used for the analyses.