Missing data
REGRESSION, SECONDARY
Missing AOC3 activity data were subject to imputation of missing values. Prior to imputation, the pattern of missing data was explored using a blinded dataset. If the pattern was monotone, a regression model was used for the imputation. Otherwise, a Markov chain Monte Carlo step was applied to create monotone data in multiple datasets prior to using a regression method. A decreasing 3-parameter curve model was then applied. For the secondary biomarkers, the pattern of missing values was assumed to be random, and directly handled within the mixed effects model for repeated measurements (MMRM) based on the likelihood method. If one of the three electrocardiogram cardiac cycles was missing, the arithmetic mean for this single electrocardiogram was computed using the available cardiac cycles. Imputation was not performed for safety endpoints, further exploratory biomarker and pharmacogenomic endpoints or other variables. Only patients without missing baseline data and with at least one non-missing post-baseline, on-treatment biomarker measurement for any primary, secondary or further biomarker endpoint were included in the full analysis set and the per-protocol set.