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RE: st: Help on data analysis strategy


From   "Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Help on data analysis strategy
Date   Tue, 29 Jul 2008 10:50:35 -0700

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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