PRIORS

I have previously conducted a meta-analysis of PK data that contained extensive and sparse sampling from 330 patients with a run time of ~ 4 days. I know have data from another 200 patients with sparse data. I have created the median and 95th PI from the previous model and overlaid the current data. The results demonstrate that the new data is well described by that model. When the new data is fit with that model, the data does not support using the model as some parameters were unidentifiable. I could conducted an analysis of all the data simultaneously but was interested in another method. Therefore, I have implemented $PRIOR into the model and noticed that with each successive increase of the df, the objective function significantly decreases. However, the THETA values do not change much and are different from the prior estimates used. The only other things that changed besides the objective function were the estimates and SE of the covariance terms and the BSV estimate of the peripheral volume of distribution. These values become more in line with the those observed previously, and the correlation values between them becomes stronger. My question is it justifiable to use such a high df (df=330) based on these significant decreases of objective function and covariance as the information from this meta-analysi would be highly informative. Thanks