Supplemental Methods B – Matching
In respect to variables that needed to be controlled for (in addition to inclusion-exclusion criteria), the present setting was extremely complex. For example, based on categorical covariates depicted in Figure S1, 768 strata of variable combinations could be formed. We used exact matching combined with optimal full matching based on Mahalanobis distance with age, BMI, eCrCl and CNI concentrations as (further) continuous matching variables. Exact matching achieves balance that corresponds to a fully blocked randomization. Optimal full matching also allows one-to-many matching, and when Mahalanobis is a distance measure, it approximates fully blocked randomization. The process first completes exact matching on specified covariates (forms subclasses of “treated” and “controls” exactly matched on a set of covariates) and then uses Mahalanobis distance (calculated using the entire data set) to further minimize within-subclass distances regarding covariates not included in exact matching [1-3]. Choice of (categorical) covariates for exact matching was guided by the intention to retain as many as possible “treated” subjects, but also on their practical importance: hence, exact matching was in respect to the type of CNI (CsA or tacrolimus),UGT2B7 and UGT1A9 SNPs.