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Re: st: RE: unpaired regression


From   "Winfield Scott Burhans" <[email protected]>
To   [email protected]
Subject   Re: st: RE: unpaired regression
Date   Tue, 11 May 2004 19:20:56 -0400 (EDT)

There is a ttest with adjustment for clustering by Jeph Herrin on SSC,
would this be what you are lookingfor?


      cltest is a pair of programs for analyzing data that have been
      randomized in clusters.  clttest compares the means of two sets
      of grouped outcomes, with optional support for group-stratified
      outcomes. clchi2 estimates an adjusted chi-square test of
      independence for 2xk groups, with optional support for
      group-stratified outcomes. Adjustments for clustering are taken
      from Donner & Klar (Cluster Randomization Trials in Health
      Services Research, 2000), with the CLCHI2 program generalized to
      the 2xk case. chchi2 uses loneway to calculate the inter-cluster
      correlation, which in Stata 6.0 and higher includes a correction
      for imbalanced groups.

      Distribution-Date: 20021204

      Author: Jeph Herrin
      Support: email [email protected]

Buzz Burhans
[email protected]



>> In the original line-up, the coefficient of determination is
>> 0.968.  In the
>> second one above, its 0.8.
>>
>>
>> -----Original Message-----
>> From: Scott Merryman [mailto:[email protected]]
>> Sent: Monday, May 10, 2004 6:42 PM
>> To: [email protected]
>> Subject: st: RE: unpaired regression
>>
>> How about lining up the measurements?
>>
>> Something like
>>
>> . l
>>
>>      +-------------------------+
>>      | batch   assay1   assay2 |
>>      |-------------------------|
>>   1. | Btch1     5400        . |
>>   2. | Btch1     5320        . |
>>   3. | Btch1     5670        . |
>>   4. | Btch1        .       .9 |
>>   5. | Btch1        .     .905 |
>>      |-------------------------|
>>   6. | Btch1        .     .898 |
>>   7. | Btch2     8600        . |
>>   8. | Btch2     7840        . |
>>   9. | Btch2     7550        . |
>>  10. | Btch2        .     .962 |
>>      |-------------------------|
>>  11. | Btch2        .     .955 |
>>  12. | Btch2        .     .943 |
>>      +-------------------------+
>>
>> . by batch: replace assay2 = assay2[_n +3]
>> (12 real changes made, 6 to missing)
>>
>> . drop if assay1 == .
>> (6 observations deleted)
>>
>> . l
>>
>>      +-------------------------+
>>      | batch   assay1   assay2 |
>>      |-------------------------|
>>   1. | Btch1     5400       .9 |
>>   2. | Btch1     5320     .905 |
>>   3. | Btch1     5670     .898 |
>>   4. | Btch2     8600     .962 |
>>   5. | Btch2     7840     .955 |
>>      |-------------------------|
>>   6. | Btch2     7550     .943 |
>>      +-------------------------+
>>
>>
>> Scott
>>
>> ________________________________________
>> From: [email protected]
>> [mailto:[email protected]] On Behalf Of
>> Wallace, John
>> Sent: Monday, May 10, 2004 7:48 PM
>> To: '[email protected]'
>> Subject: st: unpaired regression
>>
>> I have two measures of batch performance on which I'd like to
>> perform a
>> regression.  The measurements are taken on separate samples
>> from the batch,
>> and typically look something like:
>>             Assay1 Assay2
>> Btch1    5400
>> Btch1    5320
>> Btch1    5670
>> Btch1                0.900
>> Btch1                0.905
>> Btch1                0.898
>> Btch2    8600
>> Btch2    7840
>> Btch2    7550
>> Btch2                0.962
>> Btch2                0.955
>> Btch2                0.943
>> ...etc (on for multiple batches which show correlated
>> measures for the two
>> assays)
>> -collapse- ing them to batch averages and then performing the
>> regression is
>> one approach, but it doesn't take variance of the measures
>> themselves into
>> account in the regression.  Is there a system for performing
>> this type of
>> analysis?
>>
>
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