Propensity Score-Matched Survival Analysis

Categorical variables were compared using the chi-square test. Non-normally distributed data were analyzed using the Mann-Whitney U test. Survival was estimated using Kaplan-Meier survival curves and compared using the log-rank test. Multivariable analyses used Cox proportional hazards models. The conditional probability of receiving different treatment options (esophagectomy vs gastrectomy), as indicated by the propensity score, was estimated using a multivariable logistic regression model including all the variables listed in Table S2. Next, balanced cohorts using nearest-neighbor propensity score-matching (PSM) without replacement (caliper width 0.1 standard deviation) were developed.23 (link) Balance diagnostics were performed using standardized mean differences, with a value lower than 0.1 indicating good balance.23 (link) The overall survival (OS) of the matched patients who received the aforementioned treatment options was evaluated. A p value of lower than 0.05 was considered to be statistically significant. Data analysis was performed using R Foundation Statistical software (R 3.2.2) with TableOne, ggplot2, Hmisc, Matchit, and survival packages (R Foundation for Statistical Computing, Vienna, Austria), as previously reported.24 (link)