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


From   Tirthankar Chakravarty <[email protected]>
To   [email protected]
Subject   Re: st: AW: Variable estimates from GLST metaregression of observational studies
Date   Thu, 7 May 2009 16:36:56 +0100

As are mine for 5 repititions (Stata 10, XP):

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

forvalues i=1/5 {

	glst ln_rr_m dosage if  collecc==1, se(se_glst) cov(person_y case)
pfirst(study_id study_e) random
	est sto glst`i'
}

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

/* OUTPUT OMITTED */

T





On Thu, May 7, 2009 at 4:31 PM, Martin Weiss <[email protected]> wrote:
>
> <>
>
> -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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recursive class signs r, such that neither v Gen r nor Neg(v Gen r)
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