Hi Zheng, I'll take an intermediate view between Joachim and Nick.
The rich data from Phase 1 provides the ability to define the structural model and a few of the important covariates. The control of Phase 1 gives precision that cannot be achieved in Phase 2 or 3 studies. But, there are usually important differences between Phase 1 and later phase populations that makes the later phase separately important. With later phase trials, the range of covariates is expanded [1]. On top of the expanded covariate range, sometimes late-phase patient populations are categorically different than early phase [2]. In practice, this means that I fit a single model to all data. The model will allow for the dense data from Phase 1 with more inter-individual variability (IIV) terms (fix the IIV to 0 for sparse data) and the expanded covariate range with a richer set of fixed effects as the model is expanded for later phase. Finally, due to typical differences in data quality, I will often include a different residual error structure for sparse data. This approach allows the complexity of the Phase 1 structural model to carry into the richness of the late phase covariate model. [1] A specific example is that typically renal function is allowed to be lower especially when Phase 1 is in healthy subjects. [2] My true belief is that there may be unobserved covariates causing what appears to be a categorical difference. The functional impact of that belief is semantic only. In practice, the model would include a categorical parameter. Thanks, Bill On Jan 6, 2016, at 4:09, "Joachim Grevel" <[email protected]<mailto:[email protected]>> wrote: Dear Zheng, This is indeed a fundamental and recurring problem in drug development. You have rich data from Phase 1 studies (single ascending dose, multiple ascending dose, others e.g. QTc) and sparse data from Phase 3 studies. Should you mix them all in one large meta-analysis and derive the definitive popPK model for that drug/project? After years of experience, I tend to not mix Phase 1 with Phase 3 data. Phase 1 can be used to establish the first popPK model which may contain special features such as nonlinearities/saturation effects as a consequence of the wide range of doses studied. This can be the starting point for the building of a fit-for purpose model using Phase 3 data only. I have come to believe that the specific patient population(s) of Phase 3 require their own popPK model that predicts exposure without bias. This is then used in the exposure-response (E-R) modelling that is important for market approval. Only a dedicated Phase 3 popPK model, that does not carry unnecessary legacies of Phase 1 development, is fit for E-R modelling and can give the important answers about the dose rate(s) to be put in the drug label. I would be interested to hear some other opinions. Good luck, Joachim Joachim Grevel, PhD Scientific Director BAST Inc Limited Science & Enterprise Park Loughborough University Loughborough, LE11 3AQ United Kingdom Tel: +44 (0)1509 222908 www.bastinc.eu<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.bastinc.eu_&d=CwMFAg&c=UE1eNsedaKncO0Yl_u8bfw&r=4WqjVFXRfAkMXd6y3wiAtxtNlICJwFMiogoD6jkpUkg&m=wrsdorQ-9eTdtCeqy58cKOuX_NzLV7qeQgXnv6Rs89U&s=3ER4IQI_zP2M4rkqPEVwQseSkXSfoC6ux5FHzM7qeSs&e=> From: [email protected]<mailto:[email protected]> [mailto:[email protected]] On Behalf Of Zheng Liu Sent: 06 January 2016 02:03 To: [email protected]<mailto:[email protected]> Subject: [NMusers] unbalanced data set Dear all, I recently have a data set for pk parameters fitting. The issue is some patients have far more measurement points than others (i.e. a few patients have ~15 points, other patients have only 1 or 2). I speculate in the fitted parameters, those patients with many points would contribute much more than those with less points. Then the population "average" values of fitted pk parameters are not anymore average from all the patients, but more biased to those patients with many points. This is not what I expect. Of course I could take away some points from the patients with many points, in order to be comparable to less-points patients. Then I will be forced to lose some information from the data set. I just wonder are there anyone who have better proposal to solve this problem? I appreciate your help very much! Best regards, Zheng
