## BQL = 0: bad rule [Software]

Dear all,

I am happy to share my thoughts regarding handling of values BLQ in sparse sampling design:

1) In such cases I would recommend using a modeling approach using M3 method of Beal for handling values BLQ.

a. For serial sampling designs (i.e. only one sample per subject): this is just a straightforward modeling exercise (i.e. no random effects) including model selection techniques / approaches

b. For a batch design (more than one sample per subject): this would require a NLME modeling approach where it’s tricky to include Beal’s M3 but possible (I think Alex did this also in SAS )

2) If you have to stick with NCA the following heuristic approach may be useful. Lets’ assume there are n time points: t

a. At time point t

i. If mean <BLQ omit this and all subsequent time points

ii. If mean > BLQ find adequate imputations for individual BLQ values

iii. Adequate imputations for individual values BLQ can be for example derived by fitting a log-linear regression on previous sample time points (e.g. based on time points t

Please be aware that in case of more than one value BLQ at time point t

Best regards & hope this helps

Martin

I am happy to share my thoughts regarding handling of values BLQ in sparse sampling design:

1) In such cases I would recommend using a modeling approach using M3 method of Beal for handling values BLQ.

a. For serial sampling designs (i.e. only one sample per subject): this is just a straightforward modeling exercise (i.e. no random effects) including model selection techniques / approaches

b. For a batch design (more than one sample per subject): this would require a NLME modeling approach where it’s tricky to include Beal’s M3 but possible (I think Alex did this also in SAS )

2) If you have to stick with NCA the following heuristic approach may be useful. Lets’ assume there are n time points: t

_{1}< t_{2}< t_{3}< t_{i}< … < t_{n}. At each time point there are k samples where the first value BLQ is observed at time point t_{j}< t_{n}a. At time point t

_{j}: set all individual values BLQ to zero and calculate the mean at this time Point t_{j}i. If mean <BLQ omit this and all subsequent time points

ii. If mean > BLQ find adequate imputations for individual BLQ values

iii. Adequate imputations for individual values BLQ can be for example derived by fitting a log-linear regression on previous sample time points (e.g. based on time points t

_{j-3}, t_{j-2}, t_{j-1}< t_{j}) and use the corresponding predictions at time point t_{j}. This requires some thoughts regarding adequate selection of time points and likely a linear mixed model in case of batch design. Please note also that this Approach is valid only on the premise of linear PK (e.g. no concentration dependent clearance). Handling of values BLQ at the next time point t_{j+1}could be derived subsequently on a similar approach.Please be aware that in case of more than one value BLQ at time point t

_{j}, the above imputation impacts the total variability of concentration at this time point and likely adding some variability on the individual predicted values should be advantages (e.g. as sensitivity analysis).Best regards & hope this helps

Martin

### Complete thread:

- WNL Calculation of partial areas > tlast with BQL= 0 rule Babe_Ruth 2018-07-13 21:36 [Software]
- WNL Calculation of partial areas > tlast with BQL= 0 rule ElMaestro 2018-07-13 22:25
- BQL = 0: bad rule Helmut 2018-07-13 23:28
- BQL = 0: bad rule Babe_Ruth 2018-07-16 15:02
- BQL = 0: bad rule Helmut 2018-07-16 16:02
- BQL = 0: bad rulemartin 2018-07-16 20:03

- BQL = 0: bad rule Helmut 2018-07-16 16:02
- BQL = 0: bad rule martin 2018-07-16 20:43

- BQL = 0: bad rule Babe_Ruth 2018-07-16 15:02
- is zero positive? mittyri 2018-07-13 23:31
- sgn(0) = 0 Helmut 2018-07-13 23:58
- is zero positive? Babe_Ruth 2018-07-16 14:49