[R-sig-ME] novel correlation structure for nlme package, lme function

Edland, Steven @ed|@nd @end|ng |rom he@|th@uc@d@edu
Thu Jul 16 23:14:32 CEST 2020


Hi Thierry,  Apologies, I read your email too quickly.  This syntax for specifying the covariance structure works well for my application.  Thank you again, and sincere apologies for my hasty reply below.
Regards, Steve


Steven D. Edland, Ph.D.
Professor
Dept. of Family Medicine & Public Health
Dept. of Neurosciences
University of California, San Diego
9500 Gilman Dr. M/C 0948
La Jolla, CA 92093-0948
http://biostat.ucsd.edu/sedland.htm

________________________________
From: Edland, Steven <sedland using health.ucsd.edu>
Sent: Thursday, July 16, 2020 12:51 PM
To: Thierry Onkelinx <thierry.onkelinx using inbo.be>
Cc: r-sig-mixed-models using r-project.org <r-sig-mixed-models using r-project.org>; Yu Zhao <yuz867 using ucsd.edu>
Subject: Re: [R-sig-ME] novel correlation structure for nlme package, lme function


Thanks Thierry for your reply!  We did try that, and it does not work as best as we can tell.  It becomes a problem when the allocation ratio is not 1:1.  E.g., trials often use 2:1 treatment:control allocation ratio.  In this case the s.e. of the fixed effects slope coefficients are off, and we do not meet the nominal type I error rate.

Any experiences you can share would be appreciate, and thank you again for replying.

Regards,
Steve

Steven D. Edland, Ph.D.
Professor
Dept. of Family Medicine & Public Health
Dept. of Neurosciences
University of California, San Diego
9500 Gilman Dr. M/C 0948
La Jolla, CA 92093-0948
http://biostat.ucsd.edu/sedland.htm

________________________________
From: Thierry Onkelinx <thierry.onkelinx using inbo.be>
Sent: Thursday, July 16, 2020 11:53 AM
To: Edland, Steven <sedland using health.ucsd.edu>
Cc: r-sig-mixed-models using r-project.org <r-sig-mixed-models using r-project.org>; Yu Zhao <yuz867 using ucsd.edu>
Subject: Re: [R-sig-ME] novel correlation structure for nlme package, lme function

Dear Steve,

I'd create two variables
time_treatment <- ifelse(group == "treatment", time, 0)
time_placebo <- ifelse(group != "treatment", time, 0)

And then use ~ time_treatment + time_placebo | id as random effect.

This should give you the two random slope, each with it own variance.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be<mailto:thierry.onkelinx using inbo.be>
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www.inbo.be<http://www.inbo.be>

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Op do 16 jul. 2020 om 20:35 schreef Edland, Steven <sedland using health.ucsd.edu<mailto:sedland using health.ucsd.edu>>:

Hello my programmer friends.  I am wondering if anyone has written a corStruct for this lme call:

lme(y~time*group, random= ~time|id)

In this call, the covariance structure implied by (time|id) is assumed constant across groups.  We would like to model these random effects separately in the two groups.

Motivation:  In a clinical trail with an _effective_ treatment, response to treatment will  be variable (variance of the random slopes will be greater in the treatment arm).


Thank you in advance for any thoughts where I might find such a corStruct.

Steve Edland & Yu Zhao


Sincerely,
Steve Edland

Steven D. Edland, Ph.D.
Professor
Dept. of Family Medicine & Public Health
Dept. of Neurosciences
University of California, San Diego
9500 Gilman Dr. M/C 0948
La Jolla, CA 92093-0948
http://biostat.ucsd.edu/sedland.htm

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