- On 16 Jan 2004 at 13:13:27, Eva Dam (EDA.-a-.Neurosearch.dk) sent the message
Dear all

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Does anyone have experience in exclusion of outlier data pointsin

pk-studies?

We use 4 animals at every time point.

Example of a data set:

At t = 60 min

Concentrations (ng/ml): 822, 759, 4288, 800.

Howconfidently can Iexclude the data point 4288 ng/ml?

References to any relevant publications will be highly appreciated.

Best regards,

Eva Dam

Department of Bioanalysis

NeuroSearch

Denmark - On 16 Jan 2004 at 19:43:42, "J.M.Lanao" (jmlanao.-at-.usal.es) sent the message

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Dear Eva

Robust fitting using an iteratively reweighted least squares algorithm

is a good tool to solve the outliers problem. Many software as SYSTAT

and other programs can be used for this purpose.

Best regards.

Jos\0xC8 M. Lanao.

Dpt. Pharmacy and Pharmaceutical Technology

University of Salamanca

Spain - On 17 Jan 2004 at 11:04:58, dekaidek.aaa.just.edu.jo sent the message

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Outliers can be detected statistically using Z-score. A Z-score value

more than 3 or less

than -3 is considered an outlier value. Note that some studies may need

logarithmic

transformation of data before analysis. - On 17 Jan 2004 at 09:22:23, daniel.martinez.aaa.ipsen.com sent the message

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Dear Eva,

I think that only doing an exploration of your results you can be

confidently to exclude this value for your calculations but if you

prefer to

be sure you can apply a Dixon's test (test specific to find outliers). I

took the liberty of doing the test with your data and I can say you

that its

an outlier point. You can find this test in the statistical literature.

I think that it's important to know if its the complete kinetic of this

animal that have an estrange behaviour or only this sample at 60'

because in

the first case you can exclude the animal for your calculations when you

find the problem (more dose administered, animal with the minor

weight,...).

I hope it helps.

Sincerely,

Daniel Mart\0xCCnez

RIA Laboratory

Metabolism & Pharmacokinetics Service

Research & Development Department

IPSEN PHARMA, S.A.

Ctra. Laure\0x2021 Mir\0xDB 395

Sant Feliu de Llobregat, Barcelona, Spain

Tel\0xC8f.: 936858100

daniel.martinez.at.beaufour-ipsen.com - On 20 Jan 2004 at 11:29:15, "J.H.Proost" (J.H.Proost.aaa.farm.rug.nl) sent the message

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Dear Dr. Dam,

Exclusion of outlier data is a very tricky topic, that has been dealt

with in this discussion group some time ago.

In my honest opinion there is no generally applicable solution for this

problem. The use of statistics may be appropriate, but is not warranted

in all cases. Omitting values deviating by more than some multiple of

the standard deviation (e.g. 3) is attractive from a practical point of

view, but it is somewhat casual, since the standard deviation is

dependent on the particular data set.

In my experience the use of iteratively reweighted least squares

fitting does not solve the problem adequately. Just as in linear

regression, nonlinear regression is quite sensitive to outliers.

As a general rule, I recommend to analyse your data both including and

excluding the possible outlier(s) (if there is more than one outlier,

the problem increasing dramatically!), and judge both solutions

carefully with respect to plausibility.

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.at.farm.rug.nl

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