<>
-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: [email protected]
[mailto:[email protected]] Im Auftrag von G Livesey
Gesendet: Donnerstag, 7. Mai 2009 17:20
An: [email protected]; [email protected]
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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*
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