“…This method can avert some potential limitations of classic propensity scoring methods (eg, inverse probability of treatment weighting and matching) with respect to the target population, covariate balance, and precision, and mimics the properties of a randomized clinical trial 31 . The propensity score was estimated using a multivariable logistic regression model based on the patient-related background factors (sex, age, body mass index, 32 smoking index, Barthel index, Charlson comorbidity index, hypertension, diabetes, chronic obstructive pulmonary disease, renal failure, liver disease, corticosteroid use before surgery, preoperative transfusion, and clinical cancer stage), treatment factors (preoperative chemotherapy, preoperative radiotherapy, field of lymph node dissection, thoracic approach, vessel reconstruction, epidural anesthesia, 27 prophylactic corticosteroid administration, 26 prophylactic neutrophil elastase inhibitor administration, duration of anesthesia, and transfusion on the day of surgery), and hospital factors (hospital type, hospital volume, hospitals’ early extubation proportion, 28 and fiscal year); Supplemental Appendix 1 Supplemental Digital Content 1, http://links.lww.com/SLA/E975 presents the detailed definitions of the background factors. Absolute standardized differences were calculated to examine the balance in the baseline covariates of patients between the 2 groups before and after adjustment: a difference of <10% was considered acceptable 33 .…”