A review of a priori regression models for XXXX maintenance dose prediction.

@entity1 @entity15649 @entity1229
A number of a priori @entity1229 dosing algorithms, derived using linear regression methods, have been proposed. Although these dosing algorithms may have been validated using @entity1 derived from the same centre, rarely have they been validated using a @entity1 cohort recruited from another centre. In order to undertake external validation, two cohorts were utilised. One cohort formed by @entity1 from a prospective trial and the second formed by @entity1 in the control arm of the EU- @entity15649 trial. Of these, 641 @entity1 were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual @entity1 stable @entity1229 dose. Predictive ability was assessed with reference to several statistics including the R-square and mean absolute error. The six regression models explained different amounts of variability in the stable maintenance @entity1229 dose requirements of the @entity1 in the two validation cohorts; adjusted R-squared values ranged from 24.2% to 68.6%. An overview of the summary statistics demonstrated that no one dosing algorithm could be considered optimal. The larger validation cohort from the prospective trial produced more consistent statistics across the six dosing algorithms. The study found that all the regression models performed worse in the validation cohort when compared to the derivation cohort. Further, there was little difference between regression models that contained pharmacogenetic coefficients and algorithms containing just non-pharmacogenetic coefficients. The inconsistency of results between the validation cohorts suggests that unaccounted population specific factors cause variability in dosing algorithm performance. Better methods for dosing that take into account inter- and intra-individual variability, at the initiation and maintenance phases of @entity1229 treatment, are needed.
