Table 4 Multivariate Cox regression analyses for the primary outcome in the propensity score–matched populations from the University of Pisa and the Albacete Hospital.

Selection bias was addressed by propensity score analysis. Briefly, this is a two-phase technique used to estimate a treatment effect in comparative groups selected by nonrandom means. In the first phase of a propensity score analysis, variables that influence selection to group assignment are used to model the probability of receiving treatment (or of being in the reference group, in this case, the baricitinib group). The resulting probability is the propensity score. In the second phase, the propensity score is used to adjust for preexisting group differences in the analysis of the relevant outcomes. There are several ways to use propensity scores such as stratification variables, matching patients on the basis of their propensity score, or their use as a weighting or adjustment variable during multivariate analysis. In the current study, each baricitinib patient was matched to a control patient on the basis of comparable propensity scores. Assuming that all relevant covariates are included in the propensity score model, the group effect observed in a propensity score analysis represents an unbiased estimate of the true treatment effect.

HR (95% CI)P
Baricitinib0.29 (0.15–0.58)0.0001
Age1.01 (0.98–1.04)0.470
Male sex1.13 (0.54–2.34)0.750
Hypertension1.31 (0.52–3.32)0.572
Diabetes0.51 (0.23–1.17)0.113
Chronic obstructive
lung disease
0.51 (0.17–1.54)0.230
Cardiovascular disease1.41 (0.68–2.92)0.351
Chronic kidney disease1.45 (0.51–4.15)0.491
Solid cancer1.18 (0.49–2.87)0.709
Charlson comorbidity
index
1.03 (0.90–1.17)0.680
Baseline PaO2/FiO21.00 (1.00–1.00)0.823
Lymphocyte count
(per μl)
1.00 (1.00–1.00)0.657
ALT1.01 (1.00–1.03)0.026
Hydroxychloroquine2.77 (0.28–27.41)0.384
Lopinavir/ritonavir1.18 (0.38–3.61)0.776
Glucocorticoids1.79 (0.60–5.34)0.299
LMWH0.10 (0.01–1.33)0.081
Antibiotics2.34 (0.29–18.90)0.427