RE: When to do transformation of data?

From: Unknown Subject: RE: [NMusers] When to do transformation of data? Date: Tue, 23 Apr 2002 16:47:24 -0500 Chuanpu and Leonid, Ken Kowalski and I have been advocating the "log-transform both sides" approach for a while. I have found it to do a nice job stabilizing the residual variability (in epsilon) as assessed by plots of the absolute value of IWRES versus IPRED. Also, I have found that the transformation helps provide better (more reasonable) estimates of the OMEGA matrix, better estimates of the absorption rate, and I can get convergence of models that failed with the Y=F*(1+EPS) or Y=F*EXP(EPS) models. A bonus with the log-transformation is that you no longer have to worry about invoking the INTERACTION option. This is because the transformation orthogonalizes the individual predictions and the residual error. Two down sides are as follows: 1) You have to back transform the results which can require post-processing 2) Occasionally the estimation of ALAG can become problematic with standard first order absorption models. I believe that the log-transform has not been frequently used by this audience, because in some instances, models have observed that a NONMEM run will "lock-up" or fail to iterate at some point. I believe that this happens primarily when the observed (apparent) lag-time (ALAG) is greater in some individuals than their first PK sampling time point. If this phenomenon occurs in a sufficient number of subjects and the ALAG parameter is not bounded above by the first sampling time, then interaction can estimate the typical ALAG value greater than the first time point, which can result in a zero prediction, a problem when taking the log. If the upper bound is fixed to the first sampling time point, then the ALAG estimate can iterate to the bound - this is also unsatisfactory. In these cases, I have found that a two-site first order absorption model can circumvent this problem and perhaps even improve the model's ability to capture Cmax! In other words, don't discount the transformation because it cause trouble in estimation. It is precisely these issues that could be an indication that the standard absorption model is unsatisfactory! Of course, not discounting that the sampling design may also not be sufficient. Matt