From: Zheng Liu <*Zheng.Liu*>

Date: Mon, 16 Nov 2015 05:07:57 +0000

Hi Matthew,

Very large standard error and bias of Vd suggest that Vd is not well identified. Or in other word, your data didn't contain sufficient information to fit Vd. Loosely speaking it is a problem of over-parameterization, because you have only one measurement point, but you try to fit 2 parameters (Vd and clearance).

Zheng

________________________________

From: owner-nmusers

Sent: Tuesday, 10 November 2015 3:12 PM

To: Kaila, Nitin; Abu Helwa, Ahmad Yousef Mohammad - abuay010; felix boakye-agyeman; nmusers

Subject: [NMusers] RE: Large errors in the estimation of volume of distribution (Vd) for sparse data

Thanks for your responses!

Nitin, I encountered an error when generating VPC by PsN. It says “No DV values found after filtering original data. At lib/tool/npc.subs.pm line 2215.” What does it mean?

Felix, Past published data suggested similar parameter estimates and models compared to my final model. This is PO and I fixed Ka at a pre-estimated value (So no estimation of fixed or random effect).

Ahmad, Yes. The CV is even larger.

Matthew

From: Abu Helwa, Ahmad Yousef Mohammad - abuay010 [mailto:ahmad.abuhelwa

Sent: Tuesday, November 10, 2015 5:34 AM

To: HUI, Ka Ho <matthew.hui

Subject: RE: Large errors in the estimation of volume of distribution (Vd) for sparse data

Hi Mathew,

Have you tried using an exponential model for vd ? like this: Vd = TEHTA(1)*EXP(ETA(1))

Ahmad.

From: felix boakye-agyeman [mailto:boakyefe

Sent: Tuesday, November 10, 2015 12:41 AM

To: HUI, Ka Ho <matthew.hui

Subject: Re: [NMusers] Large errors in the estimation of volume of distribution (Vd) for sparse data

Hello,

Do you have historical data to compare you data to? (Do you know if you are hitting a local minimum)

Is this iv or po, if its po how is your Ka?

You may also be over-parameterized due to your data

From: Kaila, Nitin [mailto:Nitin.Kaila

Sent: Tuesday, November 10, 2015 12:14 AM

To: HUI, Ka Ho <matthew.hui

Subject: RE: Large errors in the estimation of volume of distribution (Vd) for sparse data

Matthew.

Construct visual predictive check (VPC) plots, using all the estimates of the bootstrap runs, as that will be a more true estimate of overall variability in the Cp predictions.

Use the –rawres option in PsN to perform the VPC, and then compare your original final model VPC plot with the VPC plot with all estimates of the bootstrap.

Nitin

From: owner-nmusers maxnm.com<mailto:owner-nmusers

Sent: Monday, November 9, 2015 9:43 AM

To: nmusers m.com<mailto:nmusers

Subject: [NMusers] Large errors in the estimation of volume of distribution (Vd) for sparse data

Dear all,

I have some population PK data which are in general very sparse (95% have only 1 blood sample between 2 successive doses). I developed a population PK model with the one-compartment model with 1st order absorption. The progress is generally okay except that whenever a random effect, i.e. *(1+ETA(1)), is used to describe distribution of Vd, OMEGA would be estimated to be very large (around 45% in terms of CV, with 80% Shrinkage), despite statistical significance (dOF approx. -5.5). So I dropped the random effect and expressed Vd in terms of a single fixed effect. When the final model has come out, I performed bootstrap and found that most estimates are accurate except Vd, which has a very large standard error and bias (mean 232, bias 49, SE 156), while the estimates for CL and other parameters look normal. I then constructed the predictive plots for the developed model using both the original estimates (i.e. estimates using my original dataset) (#1) and estimates from one of the bootstrap runs which has an extreme estimate of Vd (9xx) (#2), and found out that the two plots of plasma profiles are quite different in terms of the shape (#1 is “taller”, #2 is much flatter) but have similar average Cp.

These seem to be suggesting that given my sparse data, it is impossible to require accurate estimations of both CL and Vd. Apart from fixing Vd to a fixed value, is there any other possible solutions? Or is there anything that I might have overlooked?

Thanks and regards,

Matthew

Received on Mon Nov 16 2015 - 00:07:57 EST

Date: Mon, 16 Nov 2015 05:07:57 +0000

Hi Matthew,

Very large standard error and bias of Vd suggest that Vd is not well identified. Or in other word, your data didn't contain sufficient information to fit Vd. Loosely speaking it is a problem of over-parameterization, because you have only one measurement point, but you try to fit 2 parameters (Vd and clearance).

Zheng

________________________________

From: owner-nmusers

Sent: Tuesday, 10 November 2015 3:12 PM

To: Kaila, Nitin; Abu Helwa, Ahmad Yousef Mohammad - abuay010; felix boakye-agyeman; nmusers

Subject: [NMusers] RE: Large errors in the estimation of volume of distribution (Vd) for sparse data

Thanks for your responses!

Nitin, I encountered an error when generating VPC by PsN. It says “No DV values found after filtering original data. At lib/tool/npc.subs.pm line 2215.” What does it mean?

Felix, Past published data suggested similar parameter estimates and models compared to my final model. This is PO and I fixed Ka at a pre-estimated value (So no estimation of fixed or random effect).

Ahmad, Yes. The CV is even larger.

Matthew

From: Abu Helwa, Ahmad Yousef Mohammad - abuay010 [mailto:ahmad.abuhelwa

Sent: Tuesday, November 10, 2015 5:34 AM

To: HUI, Ka Ho <matthew.hui

Subject: RE: Large errors in the estimation of volume of distribution (Vd) for sparse data

Hi Mathew,

Have you tried using an exponential model for vd ? like this: Vd = TEHTA(1)*EXP(ETA(1))

Ahmad.

From: felix boakye-agyeman [mailto:boakyefe

Sent: Tuesday, November 10, 2015 12:41 AM

To: HUI, Ka Ho <matthew.hui

Subject: Re: [NMusers] Large errors in the estimation of volume of distribution (Vd) for sparse data

Hello,

Do you have historical data to compare you data to? (Do you know if you are hitting a local minimum)

Is this iv or po, if its po how is your Ka?

You may also be over-parameterized due to your data

From: Kaila, Nitin [mailto:Nitin.Kaila

Sent: Tuesday, November 10, 2015 12:14 AM

To: HUI, Ka Ho <matthew.hui

Subject: RE: Large errors in the estimation of volume of distribution (Vd) for sparse data

Matthew.

Construct visual predictive check (VPC) plots, using all the estimates of the bootstrap runs, as that will be a more true estimate of overall variability in the Cp predictions.

Use the –rawres option in PsN to perform the VPC, and then compare your original final model VPC plot with the VPC plot with all estimates of the bootstrap.

Nitin

From: owner-nmusers maxnm.com<mailto:owner-nmusers

Sent: Monday, November 9, 2015 9:43 AM

To: nmusers m.com<mailto:nmusers

Subject: [NMusers] Large errors in the estimation of volume of distribution (Vd) for sparse data

Dear all,

I have some population PK data which are in general very sparse (95% have only 1 blood sample between 2 successive doses). I developed a population PK model with the one-compartment model with 1st order absorption. The progress is generally okay except that whenever a random effect, i.e. *(1+ETA(1)), is used to describe distribution of Vd, OMEGA would be estimated to be very large (around 45% in terms of CV, with 80% Shrinkage), despite statistical significance (dOF approx. -5.5). So I dropped the random effect and expressed Vd in terms of a single fixed effect. When the final model has come out, I performed bootstrap and found that most estimates are accurate except Vd, which has a very large standard error and bias (mean 232, bias 49, SE 156), while the estimates for CL and other parameters look normal. I then constructed the predictive plots for the developed model using both the original estimates (i.e. estimates using my original dataset) (#1) and estimates from one of the bootstrap runs which has an extreme estimate of Vd (9xx) (#2), and found out that the two plots of plasma profiles are quite different in terms of the shape (#1 is “taller”, #2 is much flatter) but have similar average Cp.

These seem to be suggesting that given my sparse data, it is impossible to require accurate estimations of both CL and Vd. Apart from fixing Vd to a fixed value, is there any other possible solutions? Or is there anything that I might have overlooked?

Thanks and regards,

Matthew

Received on Mon Nov 16 2015 - 00:07:57 EST