- On 20 Feb 2006 at 19:15:51, "Vikesh Kumar Shrivastav" (vikeshk.shrivastav.-a-.ranbaxy.com) sent the message

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Dear All,

I have a question regarding Interpretation of Results for a

Bioequivalence Study.

For one of our dataset,when Statistical Analysis (ANOVA, Calculation

of 90% CI) is performed on untransformed data, it Fails to show

Bioequivalence.

However for after Log Transformation on the same dataset, study shows

Bioequivalence.

Can anyone help me understanding the reason for this behaviour.

Thanks in Advance.

Regards,

Vikesh S - On 21 Feb 2006 at 09:17:15, "Sergio F. Sanchez Bruni" (ssanchez.at.vet.unicen.edu.ar) sent the message

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Dear Vikesh,

My thought is you probably have high variability on the raw

untransformed data. Log transformation trends to decrease the data

variability and this may be the reason of the results you have

obtained when applied ANOVA.

Hope this can clarify something,

Best regards,

Prof. Sergio F. Sanchez Bruni

Research Scientist of CONICET

Laboratory of Pharmacology

Faculty of Veterinary Medicine

UNCPBA-Tandil (7000), Argentina - On 22 Feb 2006 at 09:29:38, "Hans Proost" (j.h.proost.aaa.rug.nl) sent the message

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The following message was posted to: PharmPK

Dear Vikesh and Sergio,

The reason for using a log transformation in bioequivalence testing

is that

it is likely that AUC values are (at least close to) log-normally

distributed, mainly because clearance is usually (close to) log-normally

distributed between subjects. A log-normal distribution implies that the

log-transformed values are normally distributed. And when AUC values are

log-normally distributed, one should apply ANOVA and any other

statistical

test on the log-transformed values.

Sergio wrote:

> Log transformation trends to decrease the data variability

This is indeed more or less the case, but when you write this, one could

think of some magic trick to decrease data variability. This is not

the case

here. As stated above, log-transformation is the right thing to do if

the

data are (close to) log-normally distributed.

Best regards,

Hans Proost

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.-a-.rug.nl - On 22 Feb 2006 at 14:53:03, "Sergio F. Sanchez Bruni" (ssanchez.at.vet.unicen.edu.ar) sent the message

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Dear Hans and Vikesh,

I do agree your thought, you are right and explained very well the

why of using log data transformation in bioquivalence studies.

But I think we did not fully reply the Vikesh's question which was:

"For one of our dataset,when Statistical Analysis (ANOVA,

Calculation of 90% CI) is performed on untransformed data, it Fails

to show Bioequivalence. However for after Log Transformation on the

same dataset, study shows Bioequivalence."

If it is true that my phrase induces to a magic trick for forcing

some results, I cannot deny that it is frequently used for that

reason. When data variability are very low there is a high

likelihood that ANOVA testing of the PK parameters is consistent

for transformed and untransformed data giving the same results of

bioequivalence.

Hans, I'm very sorry and don't get me wrong please, but how do you

know that there is not variability enough on the Pk parameters

obtained for the Vikesh's study.

Vikesh I'd like to hear a word from you. .

Best regards,

Prof. Sergio F. Sanchez Bruni

Research Scientist of CONICET

Laboratory of Pharmacology

Faculty of Veterinary Medicine

UNCPBA-Tandil (7000), Argentina

Telefax:+54- (0)2293-422357/426667 - On 23 Feb 2006 at 15:43:03, =?ISO-8859-1?Q?Helmut_Sch=FCtz?= (helmut.schuetz.-a-.bebac.at) sent the message

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The following message was posted to: PharmPK

Dear Nav!

you wrote:

>Is anyone in the group familiar with whether or not data for BE/BA

>type studies should be ln-transformed prior to outlier analysis.

>

Yes ;-)

>I am using Grubb's test which is based on the Z ratio and depending

>on whether the data is transformed or not, there is a slight

>variation in the results.

>

<>

What /exactly/ are you testing right now?

Since you are interested in doing the final analysis in the log-scale

you may test the individual ratios of untransformed responses (or

differences of logs).

Data mining does not make sence in a confirmatory trial (e.g.,

trying different statistical tests or playing around with

distributional assumptions).

What variant of Grubb's test are you using - the one detecting

1 outlier or the one detecting 2 outliers?

Anyway in BE analyses we are not interested in the individual

BE-ratios - only reminds me on FDA's 75/75-rule ;-)

If you are interested whether your statistical assumptions for

ANOVA are valid, you should primarily look on the intra-subjects

residuals:

Shapiro-Wilk for normality and for outliers maybe Hund's test, or

Tukey's sum-differnce plot, or the Mahalanobis distance, or ...)

best regards,

Helmut

--

Helmut Schuetz

BEBAC

Consultancy Services for Bioequivalence and Bioavailability Studies

Neubaugasse 36/11

1070 Vienna/Austria

tel/fax +43 1 2311746

http://BEBAC.at

Bioequivalence/Bioavailability Forum at http://forum.bebac.at

http://www.goldmark.org/netrants/no-word/attach.html - On 23 Feb 2006 at 09:15:33, "J.H.Proost" (J.H.Proost.-a-.rug.nl) sent the message

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The following message was posted to: PharmPK

Dear Sergio,

Thank you for your reply. This thread started with:

> "For one of our dataset,when Statistical Analysis

>(ANOVA, Calculation of 90% CI) is performed on

>untransformed data, it Fails to show Bioequivalence.

>However for after Log Transformation on the same

>dataset, study shows Bioequivalence."

This is not uncommon. A test on untransformed data is

different from a test on transformed data, and the result

can be different. If not, we would not worry about the

transformation! As stated earlier, the choice between

untransformed and transformed data should be based on the

statistical distribution of the values. As this

distribution cannot be obtained from the data in many

cases, a plausible a priori assumption is usually made. In

case of AUC and Cmax, the log-normal distribution is the

logical choice.

> If it is true that my phrase induces to a magic trick

>for forcing some results, I cannot deny that it is

>frequently used for that reason.

Yes, I suppose that this is the case. But we all know, or

should know, that one is not free to choose a statistical

test. Preferrably the test is chosen before the actual

experiment is performed. Deviation from the originally

chosen test requires a sound and solid justification.

'Forcing some results' is definitely not a sound and solid

justification!

> Hans, I'm very sorry and don't get me wrong please, but

>how do you know that there is not variability enough on

>the Pk parameters obtained for the Vikesh's study.

I didn't say that. On the contrary, I agree with your

statement that in case of low variability the test on

untransformed and transformed data will give essentially

the same results.

Best regards,

Hans Proost

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.-a-.rug.nl - On 23 Feb 2006 at 14:47:07, "Sergio F. Sanchez Bruni" (ssanchez.-a-.vet.unicen.edu.ar) sent the message

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Dear Hans,

Thanks very much for your contribution in the present discussion. You

were absolutely consistent and clear enough. Hope this discussion

helps to Vikesh to solve the concern on the study results

interpretation.

Best regards, and see you all next time.

Prof. Sergio F. Sanchez Bruni

Research Scientist of CONICET

Laboratory of Pharmacology

Faculty of Veterinary Medicine

UNCPBA-Tandil (7000), Argentina - On 24 Feb 2006 at 15:12:56, "Vikesh Kumar Shrivastav" (vikeshk.shrivastav.aaa.ranbaxy.com) sent the message

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The following message was posted to: PharmPK

Dear Hans and Sergio,

Thanks for your comments.

As far as my data set is concern i observed a high Intra CV for

Untransformed analysis,

however after ln-transformation Intra CV reduced after ln-

transformation.

May be this was the reason for dataset failing under Untransfmed

analysis and passing Bioequivalence Criteria after

ln-transformation.

Also i think this type of case occurs with dataset having small

sample size.

I guess if a large sample size is used then may be the difference in

the results of Untransformed and ln-transformed analysis should not

be very large.(I mean failing BE under untransformed state and

Passing after ln-transformation).

Another possible reason that i could see may be any occurence of some

Outlying Values in the dataset.

However I also feel that A log-normal distribution implies that the

log-transformed values are normally distributed.

Hence ANOVA results are more appropriate and reliable after Log-

Transformation.

Best Regards,

Vikesh S - On 24 Feb 2006 at 13:56:30, Prah.James.-at-.epamail.epa.gov sent the message

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The following message was posted to: PharmPK

One of the basic assumptions of ANOVA is that the variables be normally

distributed. Log transform may accomplish that. Ideally, it should be

determined that the tranform did normalize the data; if not a different

transform can be used to accomplish that.

James D. Prah, PhD

US EPA

Human Studies Division MD (58B)

Research Triangle Park, NC, 27711

919 966 6244

919 966 6367 FAX

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