Matching and data analysis
ABCG2 c.421C>A variant carriers and wt controls were matched in combined exact and optimal full matching with Mahalanobis as a distance measure using package MatchIT [21] in R [19]. The procedure allows “one-to-many” variant to control matching (andvice-versa ) and attains (exact) or approximates (Mahalanobis) balance achieved by fully blocked randomization (in respect to measured confounders) (see ESM – Supplemental Methods B, for details) [21-23]. Since in substantially different ranges (70-341 vs. 3.4-37.4 µg/L), to be used in matching CsA and tacrolimus troughs were rescaled [ln(tacrolimus) troughs rescaled to ln(CsA troughs) range by linear transformation]. Inadequately matched covariates (standardized mean difference, d ≥0.1) were adjusted for in data analysis. The variant allele effect on (ln-transformed) pharmacokinetic outcomes was estimated in raw and matched/adjusted data in frequentist (maximum likelihood with Gauss-Hermite approximation for raw data; cluster robust variance estimator for matched data) and Bayesian (4 chains, 4000 iterations, 8000 samples of the posterior, highest posterior density [HPD] credible intervals) general linear models, and was expressed as geometric means ratio (GMR). In the latter, we defined a moderately informed skeptical prior for the effect of interest consistent with thea priori hypothesis of no effect: centered at 0 for ln(GMR) with a standard deviation of 0.355. It assigns with 95% probability to a GMR between 0.5 and 2.0, and 48% probability to a GMR within the “conventional” limits of equivalence (0.80 to 1.25). We used SAS 9.4 for Windows (SAS Inc., Cary, NC) to fit frequentist models and R packagenstanarm [24] to fit Bayesian models. We used CubeX [25] to evaluate linkage disequilibrium (LD).