Statistics
There would be substantial difference in the baseline characteristics
between study groups (i.e., VTE and AF cohorts). Therefore, we performed
1:1 ratio propensity score matching to make the covariates balanced
between groups. The propensity score was the predicted probability to be
in the one group (i.e., VTE) given the values of covariates using the
multivariable logistic regression without considering interaction
effects. The variables selected to calculate propensity score were
listed in Table 1 where the follow-up year was replaced with the index
date (Table 1). The matching was processed using a greedy nearest
neighbor algorithm with a caliper of 0.2 times of the standard deviation
of the logit of propensity score, with random matching order and without
replacement. The quality of matching was checked using the absolute
value of standardized difference (STD) between the groups, where a value
less than 0.1 was considered negligible difference. We additionally
performed three propensity score matchings to compare the PE-only cohort
with the DVT-only cohort, the DVT-only cohort with the AF cohort and the
PE-only cohort with the AF cohort, respectively.
As to the time to fatal outcomes (i.e., all-cause mortality and
cardiovascular death), the risks between the groups were compared by the
Cox proportional hazard model. The incidences of time to non-fatal
outcomes (e.g., ischemic stroke or MI) between groups were compared by
the Fine and Gray subdistribution hazard model which considered
all-cause mortality a competing risk. The within-pair clustering of
outcomes after propensity score matching was accounted for by using a
robust standard error.23 A two-sided P value
<0.05 was considered statistically significant. Statistical
analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC).