st: RE: Meta-Analysis using --metan-- how to ensure comparison to ES=1 not ES=0

["Trelle Sven" <strelle@ctu.unibe.ch>]

Hi Belinda,
You will need to log-transform your hazard ratios and confidence
interval (and use the eform option in metan):

foreach var of varlist pfshr pfsll pfsul {
	gen ln_`var' = ln(`var')
}
metan ln_pfshr ln_pfsll ln_pfsul, eform effect("Hazard Ratio") fixed
lcols(study author year treatment) double by(drug) astext(50)
xlabel(0.2, 0.5, 1,  2, 5) textsize(100) title("RR of PFS") null(1)
force

Best
Sven

p.s. the null-option only refers to the graph (at least to my knowledge)


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Belinda
Butcher
Sent: Montag, 15. Oktober 2012 05:14
To: statalist@hsphsun2.harvard.edu
Subject: st: Meta-Analysis using --metan-- how to ensure comparison to
ES=1 not ES=0

Hi,

I use Stata/MP for Mac 11.2, dated 16 May 2012.  All files are up to
date..

I am using the --metan-- command (available from SSC:
http://ideas.repec.org/c/boc/bocode/s456798.html) to combine the results
of some oncology studies.

I am combining the hazard ratios for progression free survival, using
the upper and lower confidence interals).

I am using the following command:

metan pfshr pfsll pfsul, effect("Hazard Ratio") fixed lcols(study author
year treatment) double by(drug) astext(50) xlabel(0.2, 0.5, 1,  2, 5)
textsize(100) title("RR of PFS") null(1) force

I need to compare the resultant overall HR to one, rather than zero.  To
do this, I have included "null(1)" in my command.  However, when I run
the command, I get a comparison to ES=0 (see output below).

           Study     |     ES    [95% Conf. Interval]     % Weight
---------------------+--------------------------------------------------
---------------------+-
     A
1                    |    0.680     0.590     0.780         12.38
2                    |    0.758     0.661     0.869         10.33
 Sub-total           |
  I-V pooled ES      |    0.715     0.645     0.786         22.72
---------------------+--------------------------------------------------
---------------------+-
     B
3                    |    0.493     0.418     0.581         16.83
4                    |    1.190     0.720     1.960          0.29
5                    |    1.100     0.570     2.120          0.19
6                |    0.720     0.620     0.840          9.24
 Sub-total           |
  I-V pooled ES      |    0.584     0.519     0.649         26.54
---------------------+--------------------------------------------------
---------------------+-
     C
7                    |    0.540     0.420     0.710          5.32
8                    |    0.680     0.570     0.800          8.45
9                    |    0.540     0.440     0.660          9.24
10                   |    0.692     0.617     0.776         17.68
                     |    0.590     0.480     0.720          7.76
 Sub-total           |
  I-V pooled ES      |    0.628     0.580     0.676         48.45
---------------------+--------------------------------------------------
---------------------+-
     D
11                   |    1.210     0.820     1.800          0.47
12                   |    1.410     0.960     2.070          0.36
 Sub-total           |
  I-V pooled ES      |    1.298     0.930     1.665          0.83
---------------------+--------------------------------------------------
---------------------+-
     E
13                   |    0.690     0.490     1.140          1.06
14                   |    1.010     0.610     1.660          0.41
 Sub-total           |
  I-V pooled ES      |    0.779     0.502     1.055          1.46
---------------------+--------------------------------------------------
---------------------+-
Overall              |           
  I-V pooled ES      |    0.644     0.610     0.677        100.00
---------------------+--------------------------------------------------
---------------------+-
 Heterogeneity calculated by formula
  Q = SIGMA_i{ (1/variance_i)*(effect_i - effect_pooled)^2 } where
variance_i = ((upper limit - lower limit)/(2*z))^2 

Test(s) of heterogeneity:
               Heterogeneity  degrees of
                 statistic     freedom      P    I-squared**
A                     1.18         1      0.278     15.1%
B                   16.03          3      0.001     81.3%
C                    7.53          4      0.110     46.9%
D                    0.28          1      0.596      0.0%
E                    1.03          1      0.310      3.1%
Overall             46.85         14      0.000     70.1%
Overall Test for heterogeneity between sub-groups: 
                    20.79          4      0.000

** I-squared: the variation in ES attributable to heterogeneity)

Considerable heterogeneity observed (up to 81.3%) in one or more
sub-groups, Test for heterogeneity between sub-groups likely to be
invalid

Significance test(s) of ES=0

A                     z= 19.99     p = 0.000
B                     z= 17.64     p = 0.000
C                     z= 25.62     p = 0.000
D                     z=  6.92     p = 0.000
E                     z=  5.52     p = 0.000
Overall               z= 37.75     p = 0.000
------------------------------------------------------------------------
-

I have looked in Sterne's book on Meta-Analysis (Sterne J.A.C (2009)
Meta-Analysis in Stata: An updated collection from the Stata Journal),
but haven't been able to work this out.

Your help is appreciated.

Kind regards,

Belinda Butcher BSc(Hons) MBiostat PhD
Director - Biostatistics & Medical Writing WriteSource Medical Pty Ltd

PO Box 1521
Lane Cove NSW 1595

M: 0418 286 014
E: bbutcher@writesourcemedical.com.au


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