Analysis of sparse data

Date: Fri, 16 Jul 1999 16:24:59 -0400 (EDT) From: SRIRAM KRISHNASWAMI <Sriram.Krishnaswami@dupontpharma.com> Subject: Analysis of sparse data Dear NM users We have been encountering a problem with fitting a sparse data set (45 subjects, 77 concentrations) using ADVAN2 TRANS2. The initial estimates for this study were obtained from the analysis of a rich data set (5 subjects, 45 concentrations). The rich data provided estimates of CL, V and Ka with interind variabilities of 30%, 2% and 70% and a residual var of 13%. Consequently, we have been trying to fit the sparse data using these estimates but the ETAS for CL and Ka come out to be 0.002% and 175% respectively with a 42% residual variability. The estimates of CL, V and Ka seem to be OK eventhough the variabilites are high. But the Plot of IPRED vs DV shows distinct bias with higher predicted concentrations at the lower end and relatively flat at the higher end of DV. We are using FOCE to analyze the data. WRES vs DV plot also shows a bias (almost like a linear relationship) whereas WRES vs TIME plot shows no bias. As a result, we are exploring the possibility of joining the two data sets in an effort to obtain decent estimates of the inter and intra variabilities. The estimates have turned out to be reasonable with interindividual variabilites for CL, V and Ka of 40%, 16% and 70% and residual var of 37%. Moreover, the IPRED vs DV plots look much better now. The question is: Is this approach a viable or a reasonable option? and what are some of the downsides of it? The sparse data is from patients and the rich from healthy volunteers. The overall goal is to evaluate CL against some covariates such as concomitant medication and other patient related parameters. Healthy versus patients could just be one among them. Suggestions on alternative approaches are also most welcome. Thanks Sriram