Re: st: Help on data analysis strategy

[David Airey <david.airey@Vanderbilt.Edu>]

.

:Ppp <rasberry>.

To get to a simpler model, you could remove the repeated measures  
aspect of the experiment to go from a mixed model to a fixed effects  
model. So take a summary statistic of day1 to day7 that best captures  
the intent of the experiment. Something like the median score or the  
highest score over the seven day period. Then use ordered logit  
(ologit in Stata) for each question's endpoint. Be sure to include  
day0 as a covariate. I prefer this to cutting the ordinal data to  
binary data or dealing with multiple tests over days (you are not  
looking for which day shows most improvement etc.). If you have to  
have multiple tests, I'd probably deal with the 4 questions as a  
source of multiple tests. (If you have access to a statistician, and  
the 4 questions index the same construct, a statistician might be able  
to use GLLAMM or Mplus to use all your data and the 4 questions in one  
latent variable ordinal mixed model.) A possibility not requiring a  
statistician, may be to derive a summary statistic for each person as  
above, and then use a multivariate kruskal-wallis test (see umbrella  
via ssc). Probably also somersd (via ssc) could be employed here...


-Dave


On Jul 29, 2008, at 12:50 PM, Lachenbruch, Peter wrote:


> While this may be solid advice to experienced statisticians and Stata
> users, I would be a bit concerned about letting a novice loose on  
> GLLAMM
> - it's tough sledding for most experienced users.
>
> Tony
>
> Peter A. Lachenbruch
> Department of Public Health
> Oregon State University
> Corvallis, OR 97330
> Phone: 541-737-3832
> FAX: 541-737-4001
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of David Airey
> Sent: Tuesday, July 29, 2008 8:09 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: re: st: Help on data analysis strategy
>
> .
>
> My text was stripped in my reply.
>
> There is a chapter (#7) in Multilevel and Longitudinal Modeling Using
> Stata (Stata Bookstore) that describes mixed models for ordinal data
> using the command GLLAMM from ssc. This is what you need.
>
> -Dave
>
>
>
>
>
> > Dear subscribers,
> >
> > I am new to statistics and Stata, and I would like to ask for
> > advice, if I amy, regarding the type analysis for a clinical
> > experiment.
> >
> > We have 2 groups of patients, 30 in each group that undergo surgery
> > and receive either standard medication or a new medication to help
> > recovery.
> >
> > Both groups are asked 4 questions regarding for example pain,
> > inflammation ect and they are required to give an answer that gets a
> > score from 0 to 5.
> > All 4 questions are asked repeatedly for day0 (before treatment)
> > day1, day2, day3, day5 and day7.
> >
> > The objective of the study is to see if there is a difference
> > between the control and the experimental group as determined by the
> > answers to the four questions.
> >
> > Some of the ideas I have are the following:
> >
> > 1. Perform a Mann Whitney test, ordinal data, between the control
> > and the experimental group at each day and for each question
> > separately.
> >
> > 2. Define an endpoint per question. For example for the question on
> > pain define as endpoint when the answer is no pain, and use right
> > sencoring for persistent pain after day7. Perform a survival
> > analysis for each question and compare the survival curves for the 2
> > groups.
> >
> > 3. Convert to binary data, for example pain=yes for score 1 to 5,
> > and pain= no for score 0. Perform logistic regression and evaluate
> > the effect of treatment separately for every question.
> >
> > Your advice would be greatly appreciated.
> >
> > I understand the above questions might be of limited interest to
> > most subscribers but, anyways, I would like to thank you for your
> > consideration.
> >
> > Best regards,
> >
> > Nikolaos Pandis
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