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


From   Nicola Orsini <[email protected]>
To   [email protected]
Subject   st: Re: Variable estimates from GLST metaregression of observational studies
Date   Sat, 9 May 2009 15:01:57 +0200

Dear Geoff,

thanks for explaining the problem.

The problem is not related to the selection of observations to be included in the analysis (`touse' and `if' work as intended).

The problem is to keep the actual order of the selected observations within each study when pooling multiple studies. I modified one line of the -glst- command to address this point.
I will post a revised ado file on both SSC and SJ archives.

In the meantime, -sort- the dataset before running -glst- command with multiple studies or as alternative use the option tstage() which is not affected by - sort-.

* Solution 1

sort study_id , stable

glst ln_rr_m dosage if collecc==1, ///
se(se_glst) cov(person_y case) pfirst(study_id study_e)

* Solution 2

glst ln_rr_m dosage if collecc==1, ///
se(se_glst) cov(person_y case) pfirst(study_id study_e) ts(f)


Nicola



On May 7, 2009, at 5:19 PM, G Livesey wrote:


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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