Optimization results: identical time points.

[Sibojang9]

Hi Andrwe,

I am running the wafarin example scripts in the R package.
My Initial parameters(sampling points) are: 0.5 1 2 6 24 36 72 120

The Optimized Sampling Schedule are:
1e-05 1e-05 34.29 34.29 75.92 120 120 120

Since the times of some sampling points are identical. Dose that mean that the sampling points can be reduced from 6 to 3 after removing redundant points ?

Thank you,

Sibo

[andrewhooker]

Hi No. These are the optimal points based on the model and parameters you input to the calculation. If you remove points you will get less precision. You can move points from the optimal to make your design more robust to misspecification in the model or parameter values. Best regards, Andrew Andrew Hooker, Ph.D. Associate Professor of Pharmacometrics Dept. of Pharmaceutical Biosciences Uppsala University Box 591, 751 24, Uppsala, Sweden Phone: +46 18 471 4355 Mobile: +46 768 000 725 www.farmbio.uu.se/research/researchgroups/pharmacometrics/

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On 5 Jul 2017, 00:49 +0200, Sibo Jiang ***@***.***>, wrote: Hi Andrwe, I am running the wafarin example scripts in the R package. My Initial parameters(sampling points) are: 0.5 1 2 6 24 36 72 120 The Optimized Sampling Schedule are: 1e-05 1e-05 34.29 34.29 75.92 120 120 120 Since the times of some sampling points are identical. Dose that mean that the sampling points can be reduced from 6 to 3 after removing redundant points ? Thank you, Sibo — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub, or mute the thread.

[Sibojang9]

Hi Andrew,

If I keep those three points 120 120 120 hours, how can I implement this scheme? Three sampling points are all located at 120 hours. At a specific time point, we can sample only one time.

Thank you,

Sibo

[andrewhooker]

Hi I agree that the scheme is not implementable.  However, the model you provide to the design calculation knows nothing about these restrictions.  Either the optimization needs to be done with a restriction about how often sampling can occur, or the model needs to include information about correlation of parameters that are very close together (Nyberg, J., Hoglund, R., Bergstrand, M., Karlsson, M. O. and Hooker, A. C. (2012) ‘Serial correlation in optimal design for nonlinear mixed effects models’, J Pharmacokinet Pharmacodyn, 39(3), pp. 239–249. doi: 10.1007/s10928-012-9245-5.).  Another alternative is to use some windowing procedure to allow for random sampling of these three points within a window around the optimal point (discussed in https://andrewhooker.github.io/PopED/articles/intro-poped.html). Best regards, Andrew Andrew Hooker, Ph.D. Associate Professor of Pharmacometrics Dept. of Pharmaceutical Biosciences Uppsala University Box 591, 751 24, Uppsala, Sweden Phone: +46 18 471 4355 Mobile: +46 768 000 725 www.farmbio.uu.se/research/researchgroups/pharmacometrics/

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On 10 Jul 2017, 19:32 +0200, Sibo Jiang ***@***.***>, wrote: Hi Andrew, If I keep those three points 120 120 120 hours, how can I implement this scheme? Three sampling points are all located at 120 hours. At a specific time point, we can sample only one time. Thank you, Sibo — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or mute the thread.

[Sibojang9]

Hi Andrew,

Based on the above discussion, three approaches might be applicable to this scenario. My respective questions are:

1. Specification a restriction about how often sampling can occur.
At this time point, is there any built-in function of PopED to implement this?

2. Random sampling within a window around the optimal point.
In the official tutorial, there are two identical points: 10 and 10 hours. With a half hour gap, an intuitive window could be 9.75/10.25 hours, 10/10.5 hours or 9.5/10 hours. How do you think of this scheme?

3 Include information about correlation of parameters.
I am now on the way of learning the paper you cited.

I am sorry to ask so many questions. There’s no hurry to respond at once. I am happy to wait。

Sibo