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st: AW: Variable estimates from GLST metaregression of observational studies


From   "Martin Weiss" <martin.weiss1@gmx.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: AW: Variable estimates from GLST metaregression of observational studies
Date   Thu, 7 May 2009 17:31:58 +0200

<> 

-glst- can be located via -findit glst-, you should add. How do the results
differ? Mine are constant across ten repetitions...


*************
clear*

input    ln_rr_m   dosage  collecc  se_glst  person_y case study_id study_e
         0         0      1          0    39637    281       3     2
 -.0725707  1.330824      1  .09000712    40218    265       3     2
 -.1278334  2.439844      1  .09273151    40621    241       3     2
 -.2613648  4.103374      1  .09935509    40956    198       3     2
 -.4462871  7.430433      1  .10182739    41222    156       3     2

         0         0      1          0   145258    219       7     2
 -.1625189  1.212121      1  .10710753   141933    162       7     2
 -.1392621  2.203856      1  .11429413   139945    151       7     2
 -.198451   3.636364      1  .11990109   146011    132       7     2
 -.4462871  7.493112      1  .14876455   143153     77       7     2

         0         0      1          0    34750    204       8     2
 -.1508229  2.187076      1  .10585496    35154    164       8     2
 -.0618754  3.827383      1  .10787362    35196    172       8     2
 -.1625189  5.649946      1  .11324987    35488    156       8     2
 -.328504   9.112817      1  .12940233    35529    148       8     2

         0         0      1          0    23988    456       9     2
 -.0943106  1.14482       1  .07583086    25050    381       9     2
 -.1165338  2.289639      1  .08001615    24227    357       9     2
 -.1863296  3.663423      1  .03711996    26115    386       9     2 
end

compress
list, noobs  // in 1/20  sepby(id)

forv i=1/10{
	glst ln_rr_m dosage if  collecc==1, se(se_glst) cov(person_y case)
///
	pfirst(study_id study_e) random 
}
*************

Output: 


Random-effects dose-response model               Number of studies   =
4

Iterative Generalized least-squares regression       Number of obs   =
15
Goodness-of-fit chi2(14)   =    6.14                 Model chi2(1)   =
60.91
Prob > chi2                =  0.9628                 Prob > chi2     =
0.0000
----------------------------------------------------------------------------
--
     ln_rr_m |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
      dosage |  -.0484551   .0062085    -7.80   0.000    -.0606236
-.0362866
----------------------------------------------------------------------------
--
Moment-based estimate of between-study variance of the slope: tau2 =
0.0e+00


HTH
Martin

-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von G Livesey
Gesendet: Donnerstag, 7. Mai 2009 17:20
An: statalist@hsphsun2.harvard.edu; nicola.orsini@ki.se
Betreff: st: Variable estimates from GLST metaregression of observational
studies

Dear Nicola and Statalisters,

I am getting different estimates each time I run a glst command on the same
dataset in Stata and would be glad of suggestions of how to resolve the
problem.

The glst command is used here to estimate the dose-dependency of effect in
observational or relative risk data. 

The command and syntax, and extract from a dataset in use are shown below.

I am using Stata v9.2, an up-to-date version of glst and the log data
(ln_rr_m  and corresponding errors se_glst) were obtained with gen double.

I would very much appreciate help with this crucial problem.

With thanks, 
Geoff. Livesey


COMMAND AND SYNTAX:
glst ln_rr_m dosage if  collect_c==1, se(se_glst) cov(person_y case)
pfirst(study_id studyexpression) random 


DATA:
   ln_rr_m   dosage  collec~c  se_glst  person_y case study_id study_e
         0         0      1          0    39637    281       3     2
 -.0725707  1.330824      1  .09000712    40218    265       3     2
 -.1278334  2.439844      1  .09273151    40621    241       3     2
 -.2613648  4.103374      1  .09935509    40956    198       3     2
 -.4462871  7.430433      1  .10182739    41222    156       3     2

         0         0      1          0   145258    219       7     2
 -.1625189  1.212121      1  .10710753   141933    162       7     2
 -.1392621  2.203856      1  .11429413   139945    151       7     2
 -.198451   3.636364      1  .11990109   146011    132       7     2
 -.4462871  7.493112      1  .14876455   143153     77       7     2

         0         0      1          0    34750    204       8     2
 -.1508229  2.187076      1  .10585496    35154    164       8     2
 -.0618754  3.827383      1  .10787362    35196    172       8     2
 -.1625189  5.649946      1  .11324987    35488    156       8     2
 -.328504   9.112817      1  .12940233    35529    148       8     2

         0         0      1          0    23988    456       9     2
 -.0943106  1.14482       1  .07583086    25050    381       9     2
 -.1165338  2.289639      1  .08001615    24227    357       9     2
 -.1863296  3.663423      1  .03711996    26115    386       9     2 








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