Re: - Sample Size

From: "Peter I. Lee 301-594-5666 FAX 301-480-8329" <LEEP@cder.fda.gov> Subject: Re: - Sample Size Date: Mon, 12 Jun 2000 12:51:57 -0400 (EDT) Alison, One way to determine the sample size is to estimate the power of the pop PK study. This includes the alpha and beta errors. For example, if you are planning for a study to identify potential drug-drug interaction (DDI), and will calculate difference in AIC between two models, one with and the other without the potential inhibitor/inducer as the covariate. The null hypothesis can be "no clearance difference (Cl,mono -Cl, combined =0)", and the alternative hypothesis can be "a clinical significant difference in clearance (depending on the toxicity of the drug, say 30%)". With estimated inter-/intra- subject variability in PK parameters and considering study design (sampling scheme, dosing time, compliance pattern, include full profile or not, ) you can then simulate (say 200 times) the pop PK "virtue data", assuming the alternative hypothesis. Then the data can be fitted to the two models, with the significance in delta AIC set to p=0.01 or other values. The power of identifying the clearance difference is the ratio of the number of replicates showing significant DDI to the total number of replicate (200). Use the same method to estimate false positive rates, by assuming the null hypothesis (ie delta clearance = 0). You can try several designs and numbers of subjects and determine which ones will give the reasonable power (say ~80%) and false positive rate (say <5%). In addition to the power, the accuracy & precision of the estimated clearance difference will need to be considered as well. Peter Lee Associate Director, Pharmacometrics OCPB/FDA