2.6 Covariates Model
The following variables were explored as covariates for their potential to influence iberdomide PK parameters:
Data visualization was used to examine the relationship between intrinsic or extrinsic factors and subject-level PK parameters. Initial selection of covariates was guided by graphic inspection and biological plausibility. Potential covariates were tested further in Monolix.
COSSAC (conditional sampling use for stepwise approach based on correlation tests) was used for covariate search based on a previous publication 11. The iterations of COSSAC alternated between a forward selection and a backward selection, depending on the results of the correlation tests. In brief, during the forward selection step, covariate with the smallest correlation p-value (among the remaining parameter-covariate relationships) was added to the model, or the next smallest if the smallest has already been tried, and so until no correlation p-values above a threshold remain. Acceptance/rejection of the relationship was based BIC: The covariate was not retained if the criteria (0.3) did not improve (with a threshold for the likelihood ratio test). During the backward selection, among the covariates presented in the model, covariate with the highest (less significant) correlation p-value, or the next highest if the highest has already been tried, was removed, until no correlation p-values below a threshold remain. Acceptance/rejection of the relationship removal was based on BIC: The covariate was not retained if the criteria (0.01) did not improve (with a threshold for the likelihood ratio test). The algorithm continued until no forward or backward selection was possible.