mittyri
★★  

Russia,
2020-09-27 16:25
(63 d 05:33 ago)

Posting: # 21943
Views: 377
 

 differences in lambda_z calculation between PHX and PK package in R [Software]

Dear All,

some time ago I discovered the difference between PHX and 'PK' package in R regarding lambda_z calculation for sparse dataset analysis.
My question is which one looks more reasonable for you
- PHX gets means of concs and then logtransforms it to prepare for regression analysis
- PK gets logs and then calculates means for that logs to prepare for regression analysis

what do you think?

Kind regards,
Mittyri
martin
★★  

Austria,
2020-09-28 06:40
(62 d 15:19 ago)

(edited by martin on 2020-09-28 06:50)
@ mittyri
Posting: # 21945
Views: 318
 

 differences in lambda_z calculation between PHX and PK package in R

Dear mittyri,

The methods implemented in the R package PK are based on methods published in peer reviewed journals such as Non-compartmental estimation of pharmacokinetic parameters in serial sampling designs or here Non-compartmental estimation of pharmacokinetic parameters for flexible sampling designs based on log-transformation of individual values to estimate lambda_z.

The rationale for log-transforming the indivdual values is that based on this approach the variance-covariance matrix can account for values both used for derving the AUC0-t as well lambda_z used to estimate the AUC from t to infinity.

I am not aware of a publication which justifies calculation of lambda_z in case of sparse sampling based on means only as implemented in PHX.

In addition, attention should be paid to handling to values <LLOQ where you may find this paper of interest Methods for Non-Compartmental Pharmacokinetic Analysis With Observations Below the Limit of Quantification.

best regards & hope this helps

martin


PS.: I would like to use the opportunity to illustrate how important adequate handling of BLQ values are by using a theoretical example. Consider a serial sampling design (N=5 animals per time point) where all but one value is BLQ at the last time point and think about estimation of t1/2. Ignoring BLQ values at the last time point for 4 out of 5 animals will lead to a overestimated population t1/2 as the last time point is just driven by a single animal. The same is when you set BLQ values to zero as estimation of t1/2 requires some log-transformation and log of 0 is not defined and is therefore equivalent to omitting those BLQ values.
mittyri
★★  

Russia,
2020-10-06 10:31
(54 d 11:28 ago)

@ martin
Posting: # 21972
Views: 206
 

 PK package suggestions

Dear Martin,

I appreciate all the work was done for sparse analysis by you and your colleagues.
I just have 2 suggestions regarding PK package:
- is it possible to estimate lambda_z, not providing n.tail argument?
- is it possible to specify for the group argument of 'eqv()' function what which one is the test and which one is the reference?

Kind regards,
Mittyri
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