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

[Edland, Steven]

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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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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