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A binomial logistic model was fitted to age, sex, and inclusion date using treatment group as the dependent variable ( We matched subjects 1:1 on the logit transformation of the propensity score by nearest-neighbor (greedy) matching without replacement, using a caliper width equal to 0.2 x the (pooled) SD of the transformed propensity scores ( For multiple imputed datasets, matching and statistical analyses were performed separately on each resultant dataset, and the statistical estimates were finally pooled