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RE: Covariate modelling question

From: Michael Fossler <Michael.J.Fossler>
Date: Thu, 26 Feb 2015 12:33:48 +0000

Hi Fiona;

You didn’t state this, but I am assuming that you have looked at plots of partial residuals of each parameter with respect to each covariate and have determined whether a pattern exists which would help you decide whether a given covariate is worth including in the model? Also, I would assume that you’ve considered the ultimate purpose of the model , and have a pre-specified notion of which covariates you would like to test, based on some biological/medical rationale? My point being, you should not rely on p-values to select covariates – doing so will give you the situation you have just described: a large, overly-complex model.

Regardless of the technical details, if you can’t see a pattern in the residual plots with regard to a given covariate, it is unlikely to provide any meaningful reduction in the residual error of your parameter model.

Michael Fossler, Pharm. D., Ph. D., F.C.P.
Senior Director
Clinical Pharmacology Modeling and Simulation
RD Projects Clinical Platforms & Sciences

Upper Merion West
King of Prussia, PA
Email Michael.J.Fossler
Tel +1 610 270 4797
Cell 443-350-1194<> | Twitter<> | YouTube<> | Facebook<> | Flickr<>


From: owner-nmusers
Sent: Thursday, February 26, 2015 5:01 AM
To: nmusers
Subject: [NMusers] Covariate modelling question

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

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 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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Received on Thu Feb 26 2015 - 07:33:48 EST

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