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Re: [NMusers] Covariate modelling question

From: Kajsa Harling <kajsa.harling_at_farmbio.uu.se>
Date: Thu, 26 Feb 2015 11:55:42 +0100
I have looked at the output now, and am writing the answer to the mailing list in case more people are interested.

In the two different scm runs you get slightly different ofv values, and the small differences lead to different models being selected in the two runs. So why are the ofv:s different? One possible reason is that you used the retries option in PsN (automatic perturbation of initial estimates when a run is not successful) which will give different initial estimates depending on the random seed. You can check the settings of all PsN options, including the ones set by default, in the version_and_option_info.txt file in the top level of the scm run directory.

Best regards,
Kajsa

On 02/26/2015 11:28 AM, Kajsa Harling wrote:
Dear Fiona,

This sounds like a PsN question, but it is impossible to answer without the example output. The mailing list does not accept long messages or attachments, so I suggest you send the original email with all output directly to me (kajsa.harling_at_farmbio.uu.se) so that I can have a look.

Best regards,
Kajsa


On 02/26/2015 11:00 AM, Fiona Vanobberghen wrote:
I posted this message a few days ago but it doesn't seem to have been sent to the list - so I'm resending without the example output.
Best wishes
Fiona

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Dear all

I am attempting to do some covariate modelling, using the scm wizard in Pirana. I have seen some results which I wasn't expecting and would be grateful if anyone could shed any light on it for me.

Initially, I used a forward inclusion p value of 0.1 and a backward elimination p value of 0.05. This resulted in quite a complex (implausible) model (we do have a reasonably large dataset), and I decided to be more stringent, using p<0.05 for inclusion (and the same p>0.05 for elimination at the last step). As a shortcut, I could see from the output from the first attempt (with p<0.1) what I expected the final model to look like if I were to run it again with p<0.05, ie where the process would truncate. Just to double check (and verify that nothing would be eliminated at the last step), I re-ran the scm wizard with the more stringent p<0.05. And the results are not what I expected... Below I have pasted the output for the first few forward steps from each attempt. The results are essentially the same up until the third step, although we see some small differences in the OFV creeping in from the second step. However, at the fourth step, the results are completely different. This isn't what I was expecting, based on my understanding of the model selection process. Is this a known behaviour? Has anyone experienced this problem and/or know why these differences might occur? I'd be grateful for any advice.

Many thanks in advance for your help.

Best wishes
Fiona


--
Fiona Vanobberghen (née Ewings), PhD
Swiss Tropical and Public Health Institute
Socinstrasse 57, 4051, Basel, Switzerland
Tel: +41 61 284 87 41



-- 
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Kajsa Harling, PhD
System Developer
Department of Pharmaceutical Biosciences
Uppsala University

Kajsa.Harling_at_farmbio.uu.se
+46-(0)18-471 4308

http://www.farmbio.uu.se/research/researchgroups/pharmacometrics/
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-- 
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Kajsa Harling, PhD
System Developer
Department of Pharmaceutical Biosciences
Uppsala University

Kajsa.Harling_at_farmbio.uu.se
+46-(0)18-471 4308

http://www.farmbio.uu.se/research/researchgroups/pharmacometrics/
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Received on Thu Feb 26 2015 - 05:55:42 EST

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