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To all:
I am doing bioanalytical work to support PK. We often have to
compare two group data done by two different method (two different
labs, or two different analysts), and we have to document this kind
of statistical results from comparison in a view of scientific
statistical point. Therefore, we need a software which has both graph
and all the basic statistic features. I know that there are graph
and some basic features in some softwares such as "Microsoft Excel",
but we need a software which has much more features. Does anyone can
suggest me a software and give me some information?
I really appreciate any of your information!!
Nina
songn.aaa.tripharm.com
(919) 402-2627
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Date: Mon, 17 Apr 2000 08:53:17 +0200
From: furlanut.aaa.HYDRUS.CC.UNIUD.IT
Subject: Re: PharmPK Statistics software
To: PharmPK.-at-.boomer.org
X-Accept-Language: en,pdf
Give a look at SigmaStat by SPSS
http://www.spss.com/software/science/sigmastat/
Regards
Federico Pea, MD
Clinical Pharmacologist
Institute of Clinical Pharmacology & Toxicology
University of Udine
Italy
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Date: Mon, 17 Apr 2000 00:55:25 -0700 (MST)
X-Sender: ml11439.-a-.pop.goodnet.com
To: PharmPK.-at-.boomer.org
From: ml11439.-at-.goodnet.com (Michael J. Leibold)
Subject: Re: PharmPK Statistics software
Nina,
The following reference comes with statistical software which
includes most parametric and nonparametric statistical tests. It
also comes with graphical capabilities, which can be printed and
saved, but may not be publication quality.
Glantz, S.A., Primer of Biostatisics 4th ed., New York,
McGraw-Hill 1997
However, Excel has add-on capabilities which may allow the
addition of expanded graphing capabilities (a question for
Microsoft support services).
Mike Leibold, PharmD, RPh
ML11439.at.goodnet.com
---
X-Sender: jhzwafri.aaa.merle.acns.nwu.edu (Unverified)
Date: Mon, 17 Apr 2000 09:16:22 -0500
To: PharmPK.aaa.boomer.org
From: zhao wang
Subject: Re: PharmPK Statistics software
What kind of work are you doing? Are they pharmacokinetic analysis or
statistic analysis of the PK results? SAAM II can do PK and have the
statistic analysis of the PK result, for instance, the goodness the
fitting and objective function, AIC ect. which can be used as a
statistic analysis of the modeling, also the graphic features.If
you are going to do a statistic analysis of the PK result, there are
plenty softwares available, SPSS have more features. It depends on
what you are doing.
Zhao Wang
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Dear Nina:
I would stay away from Excel. Dedicated statistical packages where the
developers have paid attention to the useability and numerical stability
would be preferred. Note that for unbalanced data, Excel provides
incorrect results, and I have seen regression analyses where the
coefficients were of the wrong sign! Furthermore, the documentation is
often incorrect.
WinNonlin from Pharsight is one package that provides the statistical
functionality that you appear to be looking for. It contains an ANOVA
module that fits general linear models, of which the t-test is a special
case. In addition, the ANOVA module performs average bioequivalence
testing.
Other features of note:
* A collection of built-in PK and PD models that you can fit to your
data
* Descriptive statistics
* Data manipulation ability, based on a spreadsheet interface
* Nonparametric superposition
* Semicompartmental modeling
* Deconvolution
* Tables wizard for presentation of summary results
For further information, point your browser to http://www.pharsight.com.
A comment on the statistical analysis: A paired t-test is probably not
what you want to do. Consider the following hypothetical data for 5
independent samples:
Method 1 Method 2
50 70
70 80
90 90
110 100
130 110
A t-test (either paired or unpaired) would say the methods were
identical (t=0). The analysis I typically use is to fit the regression
model
Method1 = a + b*(Method 2) + error
where Method 2 is the reference method. If the confidence interval for
Method1(predicted)/Method1(observed) is close to 1 with no clear trend,
then you call the methods equivalent.
If you would like to discuss your comparison issues further, feel free
to email me.
Russell Reeve
Pharsight Technical Support
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Russell Reeve wrote:
> I would stay away from Excel. Dedicated statistical packages where the
> developers have paid attention to the useability and numerical stability
> would be preferred. Note that for unbalanced data, Excel provides
> incorrect results, and I have seen regression analyses where the
> coefficients were of the wrong sign! Furthermore, the documentation is
> often incorrect.
This is an important warning. I like Excel, and I never found
erroneous results. After your warning, however, I certainly will
become more critical to the Excel results. Thank you!
> A comment on the statistical analysis: A paired t-test is probably not
> what you want to do. Consider the following hypothetical data for 5
> independent samples:
>
> Method 1 Method 2
> 50 70
> 70 80
> 90 90
> 110 100
> 130 110
>
> A t-test (either paired or unpaired) would say the methods were
> identical (t=0).
This is not correct! A paired t-test (an unpaired t-test would be
inappropriate) would say that the methods are not significantly
different. This does not imply that the methods are not different,
and certainly not that the methods are identical! This is a clear
misuse of statistical information (although quite common, even in
serious science, unfortunately).
By the way, it is certainly an interesting example! And there are
indeed many way of interpreting the results completely wrong.
However, please don't use the word 'identical' in statistics, since it
does not exists in statistics (at least not at the usual level of
statistics as applied by non-statisticians).
Best regards,
Johannes H. Proost
Dept. of Pharmacokinetics and Drug Delivery
University Centre for Pharmacy
Antonius Deusinglaan 1
9713 AV Groningen, The Netherlands
tel. 31-50 363 3292
fax 31-50 363 3247
Email: j.h.proost.aaa.farm.rug.nl
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Dear Dr Proost
As t-tests are for comparing MEANS, and the means of the two samples are
IDENTICAL, both a paired and unpaired t-statistic should be equal to zero.
If not, in
which direction would there be evidence for a difference in?
If you wish to think in terms of hypothesis tests, then it is impossible
to prove anything is true (the next sample could always
contradict your conclusions). Identical, in practise, means no evidence
of
a difference. When statisticians jump on people for use of language, it is
perceived
as pedantic because only a fool would believe with absolute certainty
that the
means OF THE UNDERLYING PROCESS were equal with such a sample. This is
obvious and it is pointless
to try to edit permissible language down to Neyman-Pearson hypothesis
tests.
Russell's conclusions seem fair to me, if you want to know about
correlations, use an analysis for correlation. Failing to
reject the
null hypothesis about means doesn't tell you anything about this. Even
the
makers of Excel were kind enough to print a Pearson correlation as part of
their t-test report.
James Wright
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Dear Nina:
Since you say you often must analyse data from 2 different
labs, there may
well be 2 different assays involved, each having its own unique error
pattern. How do you deal with this problem in your analyses?
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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[Two replies - db]
Date: Tue, 25 Apr 2000 09:24:05 -0400
From: "Ning Song"
To:
Subject: PharmPK Re: Statistics software
Dear Roger:
We transfer the same method from our lab to another lab (uaually
contract lab). The method is still the same, but we need a
cross-validation between labs.
Nina
---
Date: Tue, 25 Apr 2000 13:13:53 -0400
From: "Ed O'Connor"
Reply-To: efoconnor.-at-.snet.net
Organization: PM PHARMA
X-Accept-Language: en
To: PharmPK.-at-.boomer.org
Subject: Re: PharmPK Re: Statistics software
There is a specific software sold expressly for method comparisons. It is
sold by Westgard out of Maine...I acnnot recall the name but the statistics
for comparison include random, systematic and total error, intercept and
slope, secondary stats include t and f tests. For a descitption see Chap
15 Tietz Textbook of Clinical Chemistry, Chap 15.
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Dear Dr. Wright,
Thank you for your message. You wrote:
> Identical, in practise, means no evidence of a difference.
For you, perhaps. I am not sure that everybody agrees.
If I understand you correctly, you call everything identical unless
you have some evidence of a difference?
This is indeed the usual starting point in a statistical null
hypothesis. However, 'not enough evidence for rejecting the null
hypothesis' is not identical to 'no evidence of a difference'.
> When statisticians jump on people for use of language, it is perceived as
> pedantic because only a fool would believe with absolute certainty that
> the means OF THE UNDERLYING PROCESS were equal with such a sample. This
> is obvious and it is pointless to try to edit permissible language down to
> Neyman-Pearson hypothesis tests.
I don't fully understand what point you want to make. I am not a
statisticians. I regard myself as a scientist who tries to formulate
conclusions from statistical tests correctly. And if I formulate not
correctly, I appreciate to be corrected by others.
About the fools: if you are right, there are many fools in science.
How often one reads in the Results: 'the difference between A and
B was not statistically significant', and in the Conclusion 'A is
identical to B'. This is nonsense, unless an appropriate statistical
test, e.g. a power analysis, has been performed, which is quite
seldom.
Returning to the example given by Dr. Reeve: It can be said that
the OBSERVED means are identical. This is simple logic.
This is, however, quite different from a statement about the means
OF THE UNDERLYING PROCESS. This has nothing to do with the
samples in the example of Dr. Reeve. Such a statement cannot be
made with, e.g., a t-test, irrespective of the values.
You may call this pedantic, but I say: In the world of science, one
should say what is proven, and one should not say what is not
proven.
> Russell's conclusions seem fair to me, if you want to know about
> correlations, use an analysis for correlation. Failing to
> reject the
> null hypothesis about means doesn't tell you anything about this.
I agree, of course. This was certainly not disputable.
Best regards,
Johannes H. Proost
Dept. of Pharmacokinetics and Drug Delivery
University Centre for Pharmacy
Antonius Deusinglaan 1
9713 AV Groningen, The Netherlands
tel. 31-50 363 3292
fax 31-50 363 3247
Email: j.h.proost.aaa.farm.rug.nl
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To the PharmPK list,
I support what J.H.Proost wrote. The use of pedantic statistical tests
without pedantic interpretation of the results of these tests may lead to
incorrect conclusions. In general, there is a very pedantic theory behind
many of seemingly simple statistical tests. Here, the word "pedantic" does
not have any pejorative meaning.
If a scientist form one field of science uses tools of another scientific
field she/he should be very careful in the utilization of these tools and also
in the utilization of the terminology and language of this scientific field.
With best regards,
Maria Durisova
Dipl. Engineer Maria Durisova D.Sc.
Senior Research Worker
Scientific Secretary
Institute of Experimental Pharmacology
Slovak Academy of Sciences
SK-842 16 Bratislava
Slovak Republic
http://nic.savba.sk/sav/inst/exfa/advanced.htm
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Dear Dr Proost,
At 12:52 PM 4/26/00 MET, you wrote:
>Dear Dr. Wright,
>
>Thank you for your message. You wrote:
>
>> Identical, in practise, means no evidence of a difference.
>
>For you, perhaps. I am not sure that everybody agrees.
>If I understand you correctly, you call everything identical unless
>you have some evidence of a difference?
No,that would be silly.
The notion that we had collected some data was implicit in my argument.
"No evidence of a difference" is, of course, a much stricter to criterion
than failing to reject the null hypothesis at the 5% level.
If we have collected sufficient evidence to eliminate (with some degree of
confidence) the possibility of a difference which is of practical
importance, then I might use the word identical. If I, for example,
assayed one thousand samples, spanning the range of interest, and got
exactly the same results with each method, I think the word identical would
be appropriate. If I didn't have any evidence, then I would say that I
have no evidence. In the example under discussion, I would return a
confidence interval to quantify the strength of evidence about the
difference in the means of the two samples.
Curiously, if there is no variability detected between the methods (which
does not mean there is no variability), a paired t-test would imply the
methods were identical by giving a confidence interval of zero width,
regardless of the sample size (or perhaps a division by zero error, which
is more sensible). Not all inferential procedures are this naive
thankfully. As we would know that the comparison could only be made to the
observed resolution, we could never say the two methods were absolutely
identical. This comes back to my point that it is not possible to prove
anything is absolutely true, but only true for practical purposes.
>This is indeed the usual starting point in a statistical null
>hypothesis. However, 'not enough evidence for rejecting the null
>hypothesis' is not identical to 'no evidence of a difference'.
Indeed. The latter is a subset of the former (and hence a stricter
criterion), however if we let the (entirely arbitrary) size of our test
tend to the maximum (100%) the two statements would be equivalent. After
all, there is no particular reason to go with the wimpy 5% - this is the
convention for evidence against a null hypothesis we wish to show is false.
By analogy we should use a size of 95% for a decision procedure on a null
hypothesis we wish to show is true...(not that I think this is actually a
good idea) Alternatively, if we let the sample size tend to infinity, the
two statements also become equivalent, regardless of size (sadly, not an
option).
In the example we considered, we would be unable to reject the null
hypothesis no matter what the size of our test. Of course, I do not
propose that the underlying methods are identical. We would also be unable
to reject the null hypothesis that there were differences of small
magnitude (relative to the variability in the sample) at smaller levels and
it is this line of reasoning that leads to presenting a confidence interval.
>
>> When statisticians jump on people for use of language, it is perceived as
>> pedantic because only a fool would believe with absolute certainty that
>> the means OF THE UNDERLYING PROCESS were equal with such a sample. This
>> is obvious and it is pointless to try to edit permissible language down to
>> Neyman-Pearson hypothesis tests.
>
>I don't fully understand what point you want to make. I am not a
>statisticians. I regard myself as a scientist who tries to formulate
>conclusions from statistical tests correctly. And if I formulate not
>correctly, I appreciate to be corrected by others.
>
My point was that Russell Reeve made no such claims about the underlying
processes being identical but simply pointed out that the t-test considered
the means to be identical.
On another note, hypothesis tests are not the only approach to inference.
They are decision procedures which have been extensively criticised.
>About the fools: if you are right, there are many fools in science.
>How often one reads in the Results: 'the difference between A and
>B was not statistically significant', and in the Conclusion 'A is
>identical to B'. This is nonsense, unless an appropriate statistical
>test, e.g. a power analysis, has been performed, which is quite
>seldom.
The people who make such statements are fully deserving of the criticisms
which you levelled at Dr Reeve. Not to mention failing to state the level
at which they defined statistical significance, and failing to quote the
observed p-value in case my arbitrary level differs from theirs. I am not
quite sure how a power analysis "de-nonsensifies" such conclusions, as I
thought it was something you did when designing your experiment. I guess
we can't trust the conclusion of "acceptance" from a low-powered test is
the idea. However, once we have the data we can calculate a confidence
interval and quantify the strength of evidence more precisely.
>
>Returning to the example given by Dr. Reeve: It can be said that
>the OBSERVED means are identical. This is simple logic.
>This is, however, quite different from a statement about the means
>OF THE UNDERLYING PROCESS. This has nothing to do with the
>samples in the example of Dr. Reeve. Such a statement cannot be
>made with, e.g., a t-test, irrespective of the values.
Absolutely, you can't prove the null hypothesis. Or anything else for that
matter.
>You may call this pedantic, but I say: In the world of science, one
>should say what is proven, and one should not say what is not
>proven.
>
(...one should not say what is not proven is proven. Knowing what there
isn't evidence for is quite important)
There is no such thing as absolute proof in world of science, only
evidence. However, I think we might both agree we should present the
strength of evidence, and if it was strong enough I guess you can call it
proof if I can use the word identical.
Please accept my sincere apologies for implying you were a statistician.
James Wright
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Dear Nina:
How do you do a cross-validation between labs? Further, how
do you weight
your data once it is measured and you have the results? What if the 2 labs
do not have the same error pattern?
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
********************************************************************
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