Unknown Dosing Histories

Re incomplete dosing records. You;ve taken a real tiger by the tail in this one. First off, there is no good approach. Period. If you can assume that the information is missing at random (that is, one is not missing data on those indivdiuals who tend to have kinetics different in some way than the rest), and you have a reasonable way to guess what the missing data might look like (that is, you know what distributin it is drawn from - say a uniform distribution with bounds +/- an hour from nominal dose time), then you could use a method called multiple imputation. This method essentially says to make up a bunch of complete data sets based on randomly generating the missing data (if the distribution of missing data depends on the unknown model parameters, then you have to use something called the EM algorithm - I actually don't know what you do when both are true - probably EM within each imputation). Then you analyse each imputed data set according to a fixed model (i.e., same set of covariates entering in same ways so the parameters are the same number and meaning) and use the results of all these analyses to compute a final set of estimates with appropriate standard errors (the latter are the average of the values you get from each fit, plus the std dev of the different estimates from the different fits). You can see that this is a formidable task using NONMEM. Not to mention the effect of incorrect data on the residual error model. The problem with just making up the data as you propose is that your standard errors will be too good as you are acting as though stuff you don't know was known. A former fellow of mine, Mats Carlsson, has come up wtih some things that help the residual error model in the case of errors in the data, and you can contact him (mats@c2355.ucsf.edu). You can find refrences to multiple imputation by looking for papers by Don Rubin in recent statistics literature. The above is not meant to solve your problem, but to let you know that there are no easy solutions .... Hopefully, you can examine your data and likely model and discover that the missing doses (those more than 3 doses ago) have very little effect on predictions (look at the partial derivative of the predictions wrt the missing dose(s)) - if so, then you have a non-problem and can do anything that seems reasonable. Hope I've been of some help,