estimating Ka from dataset combining rich sample study and sparse sampling study

2 messages 2 people Latest: Jun 18, 2009

RE: estimating Ka from dataset combining rich sample study and sparse sampling study

From: Jurgen Bulitta Date: June 17, 2009 technical
Dear Ethan, Your first suggestion would be a pragmatic way of moving forward. I have no personal experience with the hybrid method. Your third suggestion, using a full non-parametric approach should work better and is mathematically more consistent. This approach should not suffer from shrinkage. I would expect this algorithm to behave as follows: 1) The subjects with rich data should be essentially completely unaffected by the subjects with sparse data. 2) The subjects with sparse data should have posterior (i.e. intra-individual) probability distributions of Ka which are similar to the inter-individual distribution of Ka for the population of subjects with rich data. Depending on how the distribution of individual Ka values of the subjects with rich data look, you may or may not get a multimodal intra-individual distribution of Ka for the patients with sparse data. This may become important for the covariate relationships which you are trying to develop subsequently. Please let me know, if Roger's group or I can be of help to set you up, if you want to use NPAG for solving this task. Best wishes Juergen
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From: owner-nmusers Behalf Of Ethan Wu Sent: Wednesday, June 17, 2009 11:21 AM To: nmusers Subject: [NMusers] estimating Ka from dataset combining rich sample study and sparse sampling study Dear all, I am working on this pop PK analysis. the objective is, to explore some covariates on the exposure. the dataset has rich sampled study, with absorption phase well captured. and also sparse sampling study with only trough sample, and another sample around 1-2hr after dosing with rich sample study data, the ka and eta on Ka is well estimated using FOCE INT method and 1ct 1st order model. but when with pooled dataset, using the same model and method, eta on Ka is estimated to be almost 0, the fit to the data from rich sampled study became little worse on the peak. Is there way to keep a good estimation of Eta on Ka, which is to make sure the good capture of Cmax, at least for rich sampled subjects? with my limited knowledge, I was thinking: -- fixing Eta on ka with the estimate from rich sample study alone -- hybrid estimating methods -- nonparametric method Any comments will be highly appreciated.

RE: estimating Ka from dataset combining rich sample study and sparse sampling study

From: Stephen Duffull Date: June 18, 2009 technical
Dear Ethan I concur with Mats's comments below. As a note, from a design perspective adding additional data to an experiment cannot result in less precise parameter estimates under the assumption that the individuals from the two data sets are exchangeable. Under this assumption therefore the Sparse data should merely add information to the Rich data. That the Sparse data is affecting the parameter estimates from the Rich data suggests that the two data sets are not exchangeable (different centre, different assay, different covariates ...). Another possible way to investigate the differences between the two data sets would be to analyse them sequentially, perhaps with consideration for using the analysis from the Rich data as an informative prior for the analysis of the Sparse data and see where this leads you. Kind regards Steve -- Professor Stephen Duffull Chair of Clinical Pharmacy School of Pharmacy University of Otago PO Box 913 Dunedin New Zealand E: stephen.duffull P: +64 3 479 5044 F: +64 3 479 7034 Design software: http://www.winpopt.com
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From: owner-nmusers Behalf Of Mats Karlsson Sent: Thursday, 18 June 2009 9:17 a.m. To: 'Ribbing, Jakob'; 'Ethan Wu'; 'Jurgen Bulitta'; nmusers Cc: 'Roger Jelliffe'; 'Neely, Michael' Subject: RE: [NMusers] estimating Ka from dataset combining rich sample study and sparse sampling study Dear Ethan, Variances estimated to be zero may result from fixing off-diagonal variances to zero (i.e. not using BLOCKs in IIV). Here, however, it may be that there are systematic differences between the sparse and the rich data experiments. Maybe fasting/fed status or something else is different. If the fit to the rich data is markedly worse when including the rich data, at least one parameter is different between the two situations. I would explore what parameter(s) that would be. In addition to Jakob's suggestions below, the two data sets together may indicate a more complex structural model that a single profile indicated. Maybe you need to go to a two-compartment for example. Best regards, Mats Mats Karlsson, PhD Professor of Pharmacometrics Dept of Pharmaceutical Biosciences Uppsala University Box 591 751 24 Uppsala Sweden phone: +46 18 4714105 fax: +46 18 471 4003 From: owner-nmusers Behalf Of Ribbing, Jakob Sent: Wednesday, June 17, 2009 10:43 PM To: Ethan Wu; Jurgen Bulitta; nmusers Cc: Roger Jelliffe; Neely, Michael Subject: RE: [NMusers] estimating Ka from dataset combining rich sample study and sparse sampling study Hi Ethan, If OMEGA(?) for KA is drastically reduced when including the sparse data, then something is wrong with your model and in this case it is not the estimation method or assumption on distribution of individual parameter). Eta-shrinkage would not drastically reduce the estimate of OMEGA, since this estimate is driven by the subjects/studies which contain information on the parameter. If the sparse data is multiple dosing it may be that KA is variable between occasions, rather than between subjects (assuming the sparse data contain some information on KA). Or if the sparse data is from a less well-controlled study or a different population, it may be that increased IIV in other parts of the model (e.g. OMEGA on V) is making IIV in KA appear low for the rich study, when fitting the two studies together. If you get the covariate model in place this problem will be solved. For the simple model you have it should be quick to start out assuming that most parameters (THETAs and OMEGAs) are different between the two studies and then reduce down to a model which is stable and parsimonious. Obviously, if you eventually can explain the differences using more mechanistic covariates than study number that is of more use. Cheers Jakob