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st: RE: Re: RE: Re: Skilmack across group

From   "Lam, Chun Nok" <>
To   "" <>
Subject   st: RE: Re: RE: Re: Skilmack across group
Date   Thu, 9 Aug 2012 19:11:44 +0000

Thank you very much once again Joseph. Initially I did what you suggested as taking the pre-post difference in HbA1c values and did a Wilcoxon rank-sum test on those differences. The only problem with this is that subjects who were lost to follow-up will be excluded from the analysis. I am hoping to compare straight off the median of all the subjects available at baseline (n=128) to median of the subjects available at follow-up (n=92), as observation-wise at each time-point.  

And truly saying, instead of only baseline and follow-up, our data indeed had 3 repeated measurements (baseline, 3 month, 6 month). We were just looking at 2 time-point (baseline and 6 month) right now. So in that case the van Elteren test would be appropriate? And how should I do it in Stata (I am not too sure how the strata will define repeated measure - like the question  in my last email)?

And yes, our subjects were randomly allocated to groups at baseline, so I will looking into the Koch's "rank analysis of covariance". Thank you so much once again.

Chun Nok Jonathan Lam
Project Manager
Department of Emergency Medicine
Keck School of Medicine of USC
University of Southern California

-----Original Message-----
From: [] On Behalf Of Joseph Coveney
Sent: Thursday, August 09, 2012 11:19 AM
Subject: st: Re: RE: Re: Skilmack across group

Chun Nok Jonathan Lam wrote (excerpted):

Let me give a better picture of the data. 

I have 2 groups, intervention and control. And I measured subjects' HbA1c at baseline and follow-up:
                                 Baseline      Follow-up
Intervention           I1                      I2 
Control                     C2                     C2

I want to find out if the change of HbA1c from baseline to follow-up is significantly different cross groups.
We have a small sample, and HbA1C is not normally distributed; therefore I think a nonparametric test would be appropriate. 

The reason I looked into -skilmack- was because 1) nonparametric, 2) repeated measure, and 3) we had lost to follow-up (data structure is unbalanced). But you are right, it might not be designed to compare two treatment groups like ANOVA.
Since you suggests there could be a resampling method to get to this with -skilmack-, could you please guide me to that?

In addition, I saw a study
( used another test called van Elteren test -vanelteren-. This is what the study describe their use, which is very similar to what I hope to obtain: "The van Elteren test was used to assess differences in symptom scores of patients on propranolol and placebo stratified by time since baseline evaluation." However, I am unsure how to use the test in the right way. So I tried by making my data in long form and run:

vanelteren hba1c, by(group) strata(survey)

And my data is like: (group = 0 is control) 

Id    survey    group   hba1c
1          1              0            9.6
1          2              0            7.0           
2          1              1            8.5 
2          2              1            8.4

Since there is no id() nor repeated() in -vanelteren- like those in -skilmack- or -anova-, I am not sure how this test work in repeated measure and if the stratification is appropriate for that. 


Those authors were apparently testing for difference in change scores (difference-from-baseline values) over several posttreatment observation intervals.  You can Google "change scores" to see what at least one of their colleagues has to say about that approach. 

With a pre/post design like yours, you have only a single time since baseline evaluation, and so what those authors were apparently doing with van Elteren's test wouldn't really be applicable to your situation--you would be doing the same thing if you just took the pre-post difference in HbA1c values and did a Wilcoxon rank-sum test on those differences (change scores).

In lieu of that approach, you might want to consider using Koch's "rank analysis of covariance", especially if the patients were randomly allocated to groups (that is, baseline HbA1c concentrations should be reasonably similar between

reshape wide hba1c, i(id) j(survey)
forvalues i = 1/2 {
    egen double hba1c`i'_rank = rank( hba1c`i') } regress hba1c2_rank c.hba1c1_rank predict double hba1c2_res, residuals ranksum hba1c2_res, by(group)

A reference for the method is M. E. Stokes, C. S. Davis & G. G. Koch, _Categorical Data Analysis Using the SAS System_ Second Edition (Cary, North
Carolina: SAS Institute, 2000).  Section 7.7, pp. 174--79.

Joseph Coveney

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