Making the most of sparse clinical data by using a predictive-model-based analysis, illustrated with a stavudine pharmacokinetic study

A small-scale clinical investigation was done to quantify the penetration o f stavudine (D4T) into cerebrospinal fluid (CSF). A model-based analysis es timates the steady-state ratio of AUCs of CSF and plasma concentrations (R- AUC) to be 0.270, and the mean residence time of drug in the CSF to be 7.04 h. The analysis illustrates the advantages of a causal (scientific, predic tive) model-based approach to analysis over a noncausal (empirical, descrip tive) approach when the data, as here, demonstrate certain problematic feat ures commonly encountered in clinical data, namely (i) few subjects, (ii) s parse sampling, (iii) repeated measures, (iv) imbalance, and (v) individual design variation. These features generally require special attention in da ta analysis. The causal-model-based analysis deals with features (i) and (i i), both of which reduce efficiency, by combining data from different studi es and adding subject-matter prior information. It deals with features (iii )-(v), all of which prevent 'averaging' individual data points directly, fi rst, by adjusting in the model for interindividual data differences due to design differences, secondly, by explicitly differentiating between interpa tient, interoccasion, and measurement error variation, and lastly, by defin ing a scientifically meaningful estimand (R-AUC) that is independent of des ign. (C) 2001 Elsevier Science B.V. All rights reserved.