Sample size requirement for POP PK analysis to identify drug interactions

From: "Liu, Qi" qi_liu@merck.com Subject: [NMusers] Sample size requirement for POP PK analysis to identify drug interactions Date: Thu, 23 Feb 2006 15:55:14 -0500 Dear NONMEM users, I have a question regarding the sample size requirement for the application of POP PK analysis to identify possible drug-drug interactions. Very often, people use some cutoff number or percentage to decide whether we need to explore a concomitant drug (or a group of drugs) as a potential covariate. For example, they might specify in their data analysis plan: for any specific drug interaction, a minimum of 20 patients (or 10% of the total population in this trial) on the concomitant medication in question will be required for the analysis . It is important to have this kind of cutoff, particularly to avoid false negatives (inadequate power) and to mitigate the impact of possible bias (lack of randomization). It is also a matter of cost-benefit, since the analysis time increases almost exponentially with the number of covariates to explore. It doesnt make sense to waste time on some comedication if there isnt enough patients taking it to support a reliable conclusion. The question is how do we decide on this cutoff? I can imagine extensive simulations can give us some information on this, but the answer will vary from one case to another and it doesnt seem very practical in the industry environment due to the usually aggressive timelines. Is there a general rule of thumb? I would really appreciate if other NONMEM users could share your experience. Also, it will be very beneficial if FDA and other regulatory agencies could share their view on this. Particularly, for the claiming of lack of interaction, how do the agencies decide whether there is sufficient number of patients taking the comedication in the trial to support the claim? I noticed that some agency will also see the confidence interval associated with the lack of effect, but again, how should we decide whether the confidence interval for the lack of effect is acceptable or not? Thanks very much for your help, Qi Qi Liu, Ph.D. Merck & Co., Inc. WP75B-100 P.O. Box 4 West Point PA 19486 Tel 215 652 4096 Fax 215 993 1265

From: "Stephen Duffull" sduffull@pharmacy.uq.edu.au Subject: RE: [NMusers] Sample size requirement for POP PK analysis to identify drug inte ractions Date: Sat, 25 Feb 2006 07:57:18 +1000 Hi Qi Accurate identification of covariates is not a trivial task in any setting. Determining sample size should be considered on a case by case setting. For general consideration you could have a look at Ribbing and Jonsson JPKPD 2004;31:109-134. Essentially the size of the covariate effect, the distribution of the covariate across the study population, the complexity of the structural model (both PK and covariate) etc will influence findings. One option, as you suggest, is to perform simulations to assess the design for estimation of covariate effects. However, this process can be a little slow and requires the user to select designs. You could try assessing the design in an information theoretic approach (e.g. by assessing the population information matrix) and choosing a design that maximises your ability to identify your covariate effect (should one exist). In the latter setting there are various software that can be used to do this, e.g. POPED (which allows you to have a prior on your parameters), PFIM/PFIM_OPT (splus version) or POPT (Matlab version). Good luck. Steve ==================================================================== Stephen Duffull School of Pharmacy, University of Queensland, Brisbane 4072, Australia Tel +61 7 3365 8808, Fax +61 7 3365 1688, Email: sduffull@pharmacy.uq.edu.au www http://www.uq.edu.au/pharmacy/index.html?page=31309 Design: http://www.uq.edu.au/pharmacy/sduffull/POPT.htm MCMC: http://www.uq.edu.au/pharmacy/sduffull/MCMC_eg.htm University Provider Number: 00025B ===================================================================== _______________________________________________________