Numerical methods for the evaluation of individual bioequivalence criteria

The evaluation of individual bioequivalence (IBE) by bootstrap resampling using common statistical software, for example SAS, is extremely time consuming. In this article, an estimation procedure that can be implemented in a high level language with the same degree of accuracy as SAS is described. The necessary parameter estimating equations under both least square (LSE) and restricted maximum likelihood (REML) methods are given. The algorithms used to numerically compute these values are outlined and tested, in FORTRAN, on several simulated data sets and shown to reproduce SAS results with at least 10(-3) precision. More importantly, the REML bootstrap algorithm reduces the time taken in SAS by a factor of 20. Secondary results indicate that LSE and REML parameter estimates are similar for mild unbalancedness. PROC MIXED, with unstructured (UN) and compound symmetry heterogeneous (CSH) variance structures give the same results except when the subject-by-treatment interaction variance, sigma(2)(D), is 0 in which case CSH significantly overestimates sigma(2)(D) and underestimates the within-treatment variances. It is concluded that bootstrap evaluation of IBE is efficiently done using either the LSE or REML algorithm in FORTRAN.