RE: What are the best studies to combine?

From: "Abutarif, Malaz" malaz.abutarif@spcorp.com Subject: RE: [NMusers] What are the best studies to combine? Date: Thu, 30 Mar 2006 15:56:48 -0500 This is my take and I hope we'll get comments from others: To test your question, I would look at the following: 1. Residual plots separating (or different colors for) the data points from the different studies (i.e., one color for data points in study 1, another for study 2, ..etc). This way, you'll get a "rough idea" of any inter-study differences or if one study is on one side of the plot while the other is on the other side. 2. If you feel you have enough data, you may add the study number as a covariate 3. Look at time point distribution (time post last dose) from the different studies and try to see which study may "skew" the estimates of certain PK parameters (e.g., ka). Test for the importance of these parameters using sensitivity analysis (again, the usefulness of this may depend on the type of data you have). And before all that, look at the clinical relevance. See if it makes sense to have a different Ka for patients and Healthy volunteers, if the exposures at comparable time points and doses are similar between studies (or in the same ballpark), if there is a reason to believe that half life may be different, ...etc. And the most important question in my mind is: Why are you combining the data (for what purpose?) i.e., are you combining the data to have a bigger data set for covariate analysis or are you combining the data to have better confidence in the PK parameters for the patient study to simulate/estimate individual exposures for patients in that study? ... Depending on what the purpose of the modeling exercise is, it may or may not matter how good/bad the outcome of the points above is. This is how I personally think about the analysis when combining several data sets. I hope to hear from others how they approach this issue. Malaz