[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 21:51:12 CEST 2020
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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