- On 18 Feb 2000 at 21:00:42, Brennan (brennan.-at-.ids.net) sent the message

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I'm not sure which question/response prompted this but I figured I'd

toss my two

cents in.

My hospital has been using USCpack for several years and it had been VERY

useful. Interesting enough, the best part about MAP Baysean modeling

is not the

predictive tool but rather the fact that data points are conserved.

A picture of

what is happening with the patient can be build that is dynamic rather than

static. In using this software (or similar programs) what I've been

most excited

about is that all data points are useable. Even misdrawn serum levels can be

used to build a dynamic picture of the patient.

Being able to get a 'feel' of how your patient is handling a

drug, even when

you know that the information is not precise is incredibly valuable.

Bob - On 21 Feb 2000 at 22:33:04, "Stephen Duffull" (sduffull.-at-.pharmacy.uq.edu.au) sent the message

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Hi Bob

You comment that one of the benefits of MAP (whether USCPack

or otherwise) is that you can fit a model to all datapoints

simultaneously hence conserving all data.

> but rather the fact that data

> points are conserved.

> A picture of

> what is happening with the patient can be build

> that is dynamic rather than

> static. In using this software (or similar

> programs) what I've been

> most excited

> about is that all data points are useable.

I would argue that this model is static rather than dynamic

(in an english rather than engineering sense) since it does

not allow for a patient to change (independent of their

covariates) over a course of treatment. Eg in the case of

antibiotics it is presumed the patient will get better

during treatment which is often accompanied by a change in

their parameter values without necessarily a change in their

"predictive" covariates. How do you account for this?

Regards

Steve

=================

Stephen Duffull

School of Pharmacy

University of Queensland

Brisbane, QLD 4072

Australia

Ph +61 7 3365 8808

Fax +61 7 3365 1688 - On 22 Feb 2000 at 22:46:27, Daro Gross (maildrop.aaa.iname.com) sent the message

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[The email system misbehaved a little today...the PharmPK listserv

members may have received multiple messages...or delayed

messages...or missed messages (see the archive if this is what

happened) I think its working properly again, I hope ;-) - db]

I think the underlying message is that a physician asked to track too many

patients is better off with a tool that reports data, accurate or

inaccurate, about his patients rather none whatsoever.

This is a statement of the state of patient administration under which some

physicians must attempt to treat patients, absurd as it is on the face.

Hopefully, this will change as the law suits against the big HMOs for

allowing patients to be harmed in order to save moneys take off in the courts.

Daro Gross - On 23 Feb 2000 at 22:36:11, Brennan (brennan.at.ids.net) sent the message

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Steve,

I'm not sure how you see this as static. At each point that you

print a current analysis I suppose that you could consider that point

static. Still, with each data point you enter, the picture changes and

evolves.

The result is a very useful tool that models how an individual

patient handles a drug over time. Given a choice, I'll take Bayesean

modeling over linear regression any day you care to name. Given the

fact that aminoglycoside induced nephrotoxicity is not an issue at my

institution and that we provide very good, very therapeutic

aminoglycoside dosing, I'll stick with it for now.

Basically it just plain works.

Bob - On 24 Feb 2000 at 22:34:51, David_Bourne (david.-at-.boomer.org) sent the message

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[Two replies - db]

From: "Stephen Duffull"

To:

Subject: RE: PharmPK Re: Population Modeling

Date: Thu, 24 Feb 2000 15:43:33 +1000

X-Priority: 3 (Normal)

Importance: Normal

Bob

> I'm not sure how you see this as static. At

> each point that you

> print a current analysis I suppose that you could

> consider that point

> static. Still, with each data point you enter,

> the picture changes and

> evolves.

I'm sure the picture does evolve - but in essence the MAP

method gives equal weighting to all data points (whether

sampled an hour ago or a week ago). Hence the solution to

the Bayesian problem will be an average over the whole

course. What I am asking is whether we can expect a patient

to remain stable over a course?

> The result is a very useful tool that models

> how an individual

> patient handles a drug over time. Given a

> choice, I'll take Bayesean

> modeling over linear regression any day you care

> to name.

I am not disputing the value of MAP over other tools for

dose individualisation - however I do suggest that there

maybe caveates within the MAP process which need to be

considered from time to time.

Regards

Steve

=================

Stephen Duffull

School of Pharmacy

University of Queensland

Brisbane, QLD 4072

Australia

Ph +61 7 3365 8808

Fax +61 7 3365 1688

---

X-Sender: mentor.-at-.hardlink.com

Date: Thu, 24 Feb 2000 04:44:46 -0500

To: PharmPK.-a-.boomer.org,

Multiple recipients of PharmPK - Sent by

From: Daro Gross

Subject: Re: Population Modeling

Bob:

I presume that, in simple English, it is more useful to you to identify

patients that respond differently than other patients. Patients whose

response differs from the expected is not useful information, hence your

choice of analysis. You have discarded a tool for identifying errors or

incorrect assumptions of any kind. Are you sure that is such a wise idea,

even if your institution is of the highest quality?

Daro - On 27 Feb 2000 at 22:38:29, Brennan (brennan.at.ids.net) sent the message

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Daro,

1. It is very useful for me to identify patients that outside the norm.

Those are the ones that potentially have the most problems.

2. In a healthy 35 y/o patient I can safely dose aminoglycosides using

anything from a mg/kg recommendation all the way through the most

sophisticated modeling program you choose. They aren't the ones that are

problems. It's the 85 y/o debilitated patient that I really need to be

concerned with. The one who doesn't fit the norm.

3. While I'm in no way a statistics expert, my understanding of Bayes

Theorem is that it provides a model that responds to new data. This is a

very big plus.

4. After dosing several hundred patients with aminoglycosides over the

past couple of decades, using everything from natural log tables and a piece

of paper to programs that use Baysean modeling, I'll stick with what I've got

until something better comes along. Results are what's important.

5. The bottom line for where I practice is getting the patient well and

causing as little harm along the way as possible. The elegance of research

is a bit beyond me. I'll stick with afebrile patients with intact kidneys

along with any other analogous situations I can help provide.

6. I'll climb off my soap box now

Bob - On 2 Mar 2000 at 22:47:37, "Guillermo Bramuglia" (gbram.at.huemul.ffyb.uba.ar) sent the message

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I'm sure the picture does evolve - but in essence the MAP

method gives equal weighting to all data points (whether

sampled an hour ago or a week ago). Hence the solution to

the Bayesian problem will be an average over the whole

course. What I am asking is whether we can expect a patient

to remain stable over a course?

Regards

Steve

=================

Stephen Duffull

School of Pharmacy

University of Queensland

Brisbane, QLD 4072

Australia

Ph +61 7 3365 8808

Fax +61 7 3365 1688 - On 3 Mar 2000 at 21:59:32, David_Bourne (david.-at-.boomer.org) sent the message

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[Two replies - db]

Comments: Authenticated sender is

=46rom: "Guillermo Bramuglia"

To: PharmPK.aaa.boomer.org

Date: Fri, 3 Mar 2000 14:11:42 +0000

Subject: Re: Population Modeling

X-Confirm-Reading-To: "Guillermo Bramuglia"

X-pmrqc: 1

Priority: normal

some programs allow to weight points using an error function (such as

USC pack). This is a way to differentiate the points to fit taking

information about the analitical technique, for example. The problem

is (I think), how to include different kinds of information about the

patient that can be take into account over a course, to fit and

estimate the best parameters.

regards,

Guillermo

Dr. Guillermo Bramuglia

C=E1tedra de Farmacolog=EDa,

=46acultad de Farmacia y Bioqu=EDmica,

Universidad de Buenos Aires.

---

Date: Fri, 03 Mar 2000 20:17:38 -0500

=46rom: Brennan

X-Accept-Language: en

To: PharmPK.-a-.boomer.org

Subject: Re: Population Modeling

But as each data point is weighed, the overall picture of what is

happening also changes. All of this must be in conjunction with the

clinical assessment of each patient of course.

Let's put it this way. If anyone has a method of analysis that does a

better job in a non-research, on the floor, "I need the answer now" type

of setting, I'd love to hear about it.

=20 Bob - On 5 Mar 2000 at 22:17:32, Brennan (brennan.-a-.ids.net) sent the message

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Guillermo,

Your comments speak very well to the clinical setting. Indeed you point

out the problem itself. How best to use the analytical tools available

versus how best to treat the patient.

The number of variables involve in any given patient is huge and cannot

be modeled with the precision needed for PK/PD solutions designed for that

individual patient. There are far to many.

By defining populations, one can narrow the variables down a bit. By

sampling and remodeling, one can start to see the differences in each

patient. It is here that an optimal therapeutic regimen can be outlined.

Is it the best way? Hopefully not. Is it valuable? Most definitely!

Bob Brennan - On 6 Mar 2000 at 20:41:04, larserich.aaa.rh.dk sent the message

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Guillermo,

Your comments speak very well to the clinical setting. Indeed you

point

out the problem itself. How best to use the analytical tools available

versus how best to treat the patient.

The number of variables involve in any given patient is huge and

cannot

be modeled with the precision needed for PK/PD solutions designed for that

individual patient. There are far to many.

By defining populations, one can narrow the variables down a bit. By

sampling and remodeling, one can start to see the differences in each

patient. It is here that an optimal therapeutic regimen can be outlined.

Is it the best way? Hopefully not. Is it valuable? Most definitely!

Bob Brennan--- - On 7 Mar 2000 at 22:45:12, Roger Jelliffe (jelliffe.at.usc.edu) sent the message

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Hi Bob and Steve Duffull:

About MAP and static versus dynamic. What we have done with

the USC*PACK

software is to parameterize the models so they can change with changing

renal function and body weight. As the patient's renal function changes, so

does his kel, for example, as it is described as being a nonrenal intercept

value times a slope value (increment of Kel per unit of CCr) times the

patient's CCr at any time. That is why we developed the method for

estimating CCr from changing, and not just stable, serum creatinine values.

The model is able to follow and track the changes in the status of the

patient from dose to dose and from day to day.

One can then use MAP Bayesian fitting procedures to fit the

model to the

data of the populatioon prior model and the patient's data of dosage, CCr,

body weight, and serum concentrations, and come up with a model that can

track the behavior of the drug in the patient over time.

The interesting thing is the point you raise about what to do when the

patient's parameters change independently of the covariates. How does one

detect these changes? This is a sticker. All the procedures out there at

present that I am aware of assume that the patient is fixed and unchanging

in his/her parameter values during the fitting procedure. Even with

sequential Bayesian approaches that I know of, this is the case. For

example, if one has a cluster of 4 serum levels to fit, whether they are

fitted sequentially or all together, while the sequential method shows

changes in the Bayesian posterior during the fitting and addition of each

new data point, I am told that one has still not departed from the

hypothesis that the true patient has fixed and unchanging parameter values,

and that one has not learned anything new from the point of view of optimal

dosing than from fitting them all 4 together.

However, an interacting multiple model (IMM) approach to developing

Bayesian posteriors that can detect such a change in parameter values over

time has been developed by Dr. David Bayard in our lab. When presented with

a simulated patient in which the changes in the parameter values occur at a

stated time in the regimen, the sequential IMM approach tracked the

behavior of the simulated patient with only about half the integrated error

of the sequential MAP Bayesian or the sequential multiple model Bayesian

methods, and was able to do a reasonable job of detecting the change. This

work was presented at the SCS meetings in San Diego in January,

(Proceedings of the Society for Computer Simulation International, Health

Sciences Simulation, 2000, pp 75-83). It is being incorporated into the

new Windows version of the Multiple Model USC*PACK software which we hope

to have available for clinical use sometime this year.

Very best regards,

Roger Jelliffe

Roger W. Jelliffe, M.D. Professor of Medicine, USC

USC Laboratory of Applied Pharmacokinetics

2250 Alcazar St, Los Angeles CA 90033, USA

Phone (323)442-1300, fax (323)442-1302, email= jelliffe.-at-.hsc.usc.edu

Our web site= http://www.usc.edu/hsc/lab_apk

************** - On 10 Apr 2000 at 21:41:38, Roger Jelliffe (jelliffe.-at-.usc.edu) sent the message

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Dear Steve and Guillermo:

About the MAP Bayesian process and stability. It is nice to link PK

parameters to clinical descriptors as much as possible, and to parameterize

the parameters in terms of these descriptors or covariates as much as

possible, so that when the patient changes, and the descriptors change, one

can still follow the changing behavior of the patient. In this, there is

still the built in hypothesis that the "patient" being fitted is fixed and

unvarying thrung the period of the data analysis, even is the MAP Bayesian

analysis is being done sequentially, as each new data point becomes available.

To overcome this, and to be able to detect changing parameter

values which

are NOT accompnaied by changes in descriptors or covariates, Dave Bayard in

our lab has implemented an Interacting Multiple Model (IMM) approach to

obtaining discrete Bayesien posteriors in which the patient's parameter

values actually may jump from one set of values in the nonparametric

population discrete joint density to another. This approach comes from the

aerospace community, and has worked well in tracking a simulated patient

whose parameter values changed, with only about half the total error if the

MAP or multiple model approach. This work was presented at the CSC meetings

in San Diego in January. I am enclosing a copy of an abstract about it

which we have submitted for consideration for presentation at the PAGE

meeting in June. It appears to be a distinct improvement over the MAP

approach, and it builds on the strengths of nonparametric populationmodels

in general.

AN INTERACTIVE MULTIPLE MODEL (IMM) ESTIMATION APPROACH

TO UPDATING POSTERIORS IN PHARMOCOKINETIC MODELS HAVING CHANGING PARAMETER

VALUES

Roger W. Jelliffe1 and David S. Bayard2,

1 Laboratory of Applied Pharmacokinetics, University of Southern

California, School of Medicine, Los Angeles CA, 90033

2 Senior Research Scientist, Jet Propulsion Laboratory, Pasadena CA, 91109

This report considers updating Bayesian posterior densities for

pharmacokinetic models having changing parameter values. The prior

probability is assumed to be a discrete joint density. Parameter changes

are modeled as "jumps" from one model support point to another within the

same discrete density. Given such discrete priors, the multiple model (MM)

estimation approach provides an exact analytical solution for updating the

Bayesian posteriors. Our laboratory's earlier studies showed that the MM

estimator works well in pharmacokinetic applications where the patient's

parameters are unknown but constant.

Unfortunately, the MM estimator works less well where the patient's

parameter values vary. The IMM algorithm has emerged as an effective method

in the literature for tracking changing parameter values, and is used by

the aerospace community for tracking maneuvering targets. We implemented

the IMM sequential Bayesian algorithm in pharmacokinetic software. Its

performance was compared with the MM and MAP sequential Bayesian estimation

methods, which are used in pharmacokinetic applications where parameter

values do not change, using both simulated and real clinical data for the

drug Tobramycin.

In a simulated therapeutic scenario of changing parameter values taking

place at a stated time, the IMM approach tracked the behavior of the

simulated patient with about half the integrated total error found with the

MM and MAP methods. Further, in examining a real patient's data, in which

parameter values appeared to change significantly during therapy, the IMM

approach tracked the patient's data quite acceptably, and much better than

the MAP Bayesian approach.

While the present paper focuses on estimation, the authors' main motivation

for studying this problem is to eventually develop better controllers,

i.e., to extend the present Multiple Model active control approach to

designing dosage regimens for unstable patients with varying parameter values.

Supported in part by NIH grants LM 05401 and RR 11526

Very best regards,

Roger Jelliffe

Roger W. Jelliffe, M.D. Professor of Medicine, USC

USC Laboratory of Applied Pharmacokinetics

2250 Alcazar St, Los Angeles CA 90033, USA

Phone (323)442-1300, fax (323)442-1302, email= jelliffe.aaa.hsc.usc.edu

Our web site= http://www.usc.edu/hsc/lab_apk

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