From: Austin, Matt <maustin_at_amgen.com>

Date: Tue 08 Mar 2005 - 13:11:11 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Mar 08 13:21:23 2005

Date: Tue 08 Mar 2005 - 13:11:11 EST

Here are two possibilities, but neither tests simultaneously--others may
have those suggestions.

- If one of the variables in clinically more important than the other, consider a stepdown procedure where the more important is tested first, then if the first is successful the second is tested. Procedures such as this are well documented in the literature.
- Find a function of the two that has a clinical interpretation and model that response. Body mass index is an example of such a function (kg/m^2), but probably is not an endpoint that would be of interest in a growth study in pediatrics.

Matt Austin

Statistician

Amgen

One Amgen Center Drive

M/S 24-2-C

Thousand Oaks CA 93021

(805) 447 - 7431

"Today has the fatigue of a Friday and the desperation of a Monday" -- S. Pearce 2005

> -----Original Message-----

*> From: r-help-bounces@stat.math.ethz.ch
**> [mailto:r-help-bounces@stat.math.ethz.ch]On Behalf Of Dominik
**> Grathwohl
**> Sent: Monday, March 07, 2005 17:18 PM
**> To: r-help@stat.math.ethz.ch
**> Subject: [R] One mixed effects model for two variables
**>
**>
**> Hello R-help group,
**>
**> I need some suggestions in stating a mixed effects model. I
**> would like to
**> fit one mixed effect model to two or more variables and than
**> investigating
**> treatment effects by applying the multcomp library. I will explain my
**> problem along an example of weight and length measurements on infants.
**> Weight and length are measured at two occasions in time and for two
**> treatment groups, one group got some experimental formula and
**> the other some
**> control formula. It is also reasonable to assume that weight
**> and length are
**> correlated. Aim is to estimate the treatment effect on weight
**> and on length
**> taking multiplicity into account.
**> Below I generated some data of the proposed properties and I
**> applied also
**> two mixed effects models, one model for weight and one for
**> length. Up to now
**> I’m not able to state a model for both variables together, any
**> suggestions?
**>
**> library(nlme)
**>
**> n <- 200 # n = number of subjects
**> id <- rep(1:n, each=2) # id = subject identification number
**> t <- rep(0:1, times=n) # two occations in time
**> trt <- rep(sample(rep(0:1, times=n/2)), each=2) # two treatment groups
**> b1 <- rnorm(n, mean=0, sd=0.5) # b1 = random
**> effect of weight
**>
**> y1 <- 3.5 + rep(b1, each=2) + 0.7 * t + 0.01 * trt + 0.1 * t * trt +
**> rnorm(2*n,mean=0, sd=0.09)
**> # y1 = weight measurements, 3.5 kg at baseline,
**> # 0.7 kg more at the second time occation
**> # 0.01 = the treatment effect at baseline,
**> # the treatment effect is 0.1 kg for the
**> # experimental group at at the second time occation.
**> # The whithin subject standard deviation is 0.09.
**>
**> b2 <- 0.9 * 3/sd(b1) * b1 + rnorm(length(b1), mean=0,
**> sd=sqrt(1-0.9**2)*3)
**> # b2 = random effect for length with standard deviation = 3
**> # and correlated with the random effect of weight (b1) by r=0.9
**>
**> y2 <- 49 + rep(b2, each=2) + 2 * t - 0.05 * trt + 0.5 * t * trt +
**> rnorm(2*n,mean=0, sd=1)
**> # y2 = length measurements, 49 cm at baseline,
**> # 2 cm more at the second time occation
**> # 0.05 = the treatment effect at baseline,
**> # the treatment effect is 0.5 cm for the
**> # experimental group at at the second time occation.
**> # The whithin subject standard deviation is 1.
**>
**>
**> # data frame of the data:
**> df <- data.frame(var=as.factor(rep(0:1, each=2*n)),
**> id=c(id, id), t=as.factor(c(t, t)), trt=as.factor(c(trt,
**> trt)), y=c(y1,
**> y2))
**>
**> # grouped data object:
**> gd <- groupedData(y ~ t | id, data=df)
**>
**> # mixed effects model on weight:
**> fm1weight <- lme(y ~ t * trt, random = ~ 1 | id, data = gd,
**> subset=var==0)
**> summary(fm1weight)
**>
**> # mixed effect model on length:
**> fm1length <- lme(y ~ t * trt, random = ~ 1 | id, data = gd,
**> subset=var==1)
**> summary(fm1length)
**>
**> --
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*

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R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Mar 08 13:21:23 2005

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