The impact of compliance in pharmacokinetic studies.

In population pharmacokinetic (PK) studies, one observes just a few concentration measures spread out in time, on a sizable sample of the target population. Common-sense dictates that for estimation of a drug exposure-plasma concentration relationship, one needs accurate information on drug intake history besides the concentration measures. The population PK literature is well aware of this. Studies of simulated compliance behaviour have helped quantify the problem with naive compliance estimators and pointed towards a solution. In this paper we look at actually observed compliance patterns recorded via electronic monitoring. We simulate a documented pharmacokinetic model from the hypertensive literature on top of these and come to some interesting findings. In this clinical trial the problem of noncompliance is much more dramatic than simulated compliance patterns suggested so far. The systematic errors made by compliance naive estimators can be corrected when using timing explicit hierarchical nonlinear models and accurate information on a number of previous dose timings. When it is possible to observe irregular drug intake times in a well-controlled study, a substantial amount of precision is retrieved from the same number of data points. In general, the estimators of PK parameters benefit greatly from information that enters through greater variation in the drug-exposure process. Here we find support for the claim that noncompliance as a rich natural experiment of dosing variation can be a blessing rather than a curse from the information/learning point of view.