Hi, I have a data set collected from 10 measurements (response variables) on two groups (healthy and patient) of subjects performing 4 different tasks. In other words there are two fixed factors (group and task), and 10 response variables. I could analyze the data with aov() or lme() in package nlme for each response variable separately, but since most likely there are correlations among the 10 response variables, would it be more meaningful to run a MANOVA? However manova() in R seems not to allow an error term in the formula. What else can I try for this kind of multivariate mixed model? Also, if I want to find out which response variables (among the 10 measurements) are statistically significant in terms of acting as indicators for group difference, what kind of statistical analysis would help me sort them out? Thanks in advance, Gang
Mixed model with multiple response variables?
5 messages · Gang Chen, David Afshartous, David Duffy +2 more
Note sure how to fit the model in R, but two good references are: Fieuws & Verbeke (2006). "Pairwise fitting of mixed models for the joint modeling of multivariate longitudinal profiles," Biometrics, 62, 424-431. Fieuws et al. (2008). "Predicting renal graft failure using multivariate longitudinal profiles," Biostatistics, 9, 419-431.
On 8/5/08 3:54 PM, "Gang Chen" <gangchen6 at gmail.com> wrote:
Hi, I have a data set collected from 10 measurements (response variables) on two groups (healthy and patient) of subjects performing 4 different tasks. In other words there are two fixed factors (group and task), and 10 response variables. I could analyze the data with aov() or lme() in package nlme for each response variable separately, but since most likely there are correlations among the 10 response variables, would it be more meaningful to run a MANOVA? However manova() in R seems not to allow an error term in the formula. What else can I try for this kind of multivariate mixed model? Also, if I want to find out which response variables (among the 10 measurements) are statistically significant in terms of acting as indicators for group difference, what kind of statistical analysis would help me sort them out? Thanks in advance, Gang
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On Tue, 5 Aug 2008, Gang Chen wrote:
Hi, I have a data set collected from 10 measurements (response variables) on two groups (healthy and patient) of subjects performing 4 different tasks. In other words there are two fixed factors (group and task), and 10 response variables. I could analyze the data with aov() or lme() in package nlme for each response variable separately, but since most likely there are correlations among the 10 response variables, would it be more meaningful to run a MANOVA? However manova() in R seems not to allow an error term in the formula. What else can I try for this kind of multivariate mixed model?
You might look at the Oct 2007 R-News article on the subject. But a flexible approach is to use the sem package. David Duffy.
| David Duffy (MBBS PhD) ,-_|\ | email: davidD at qimr.edu.au ph: INT+61+7+3362-0217 fax: -0101 / * | Epidemiology Unit, Queensland Institute of Medical Research \_,-._/ | 300 Herston Rd, Brisbane, Queensland 4029, Australia GPG 4D0B994A v
Hi Gang, I suggest that you ask yourself whether or not the correlation between the response variables is of inferential interest in the subject matter. If not, then analyze them separately, correct for multiple tests somehow, and check the correlation of the residuals. If the residuals are correlated then a more efficient estimate would be possible using e.g. a relation to seemingly unrelated regression. If the residuals are uncorrelated then I think that you can keep the separate analyses. If you want to try to model the correlations between the response variables in an otherwise mixed-effects framework, some nice work was done by Daniel Hall (U of Georgia) on forestry data, published in Biometrics, if I recall correctly. I also tried out some ideas in a 2004 article published in the Canadian Journal of Forest Research. Cheers, Andrew
On Tue, 5 Aug 2008, Gang Chen wrote: Hi, I have a data set collected from 10 measurements (response variables) on two groups (healthy and patient) of subjects performing 4 different tasks. In other words there are two fixed factors (group and task), and 10 response variables. I could analyze the data with aov() or lme() in package nlme for each response variable separately, but since most likely there are correlations among the 10 response variables, would it be more meaningful to run a MANOVA? However manova() in R seems not to allow an error term in the formula. What else can I try for this kind of multivariate mixed model?
Andrew Robinson Department of Mathematics and Statistics Tel: +61-3-8344-6410 University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599 http://www.ms.unimelb.edu.au/~andrewpr http://blogs.mbs.edu/fishing-in-the-bay/
On 06/08/2008, at 5:54 AM, Gang Chen wrote:
Hi, I have a data set collected from 10 measurements (response variables) on two groups (healthy and patient) of subjects performing 4 different tasks. In other words there are two fixed factors (group and task), and 10 response variables. I could analyze the data with aov() or lme() in package nlme for each response variable separately, but since most likely there are correlations among the 10 response variables, would it be more meaningful to run a MANOVA? However manova() in R seems not to allow an error term in the formula. What else can I try for this kind of multivariate mixed model? Also, if I want to find out which response variables (among the 10 measurements) are statistically significant in terms of acting as indicators for group difference, what kind of statistical analysis would help me sort them out?
This looks like a multilevel model, with your measurements nested within subject. The difference to typical models is that each outcome will need a different variance, which I think is possible in LME. A GEE might work (as an alternative to multilevel GEE which should) using the robust SE to cope with the model misspecification. Ken