Hello everybody,
I am performing several meta-analyses using the metan command in STATA v 18.0 for a systematic review ob observational studies focusing on prognostic factors. As most meta-analyses include 3-6 studies, the I^2 point estimate is unreliable, so that I wanted to include the confidence intervals of I^2 into the forestplots rather than (or in addition to) the p-values.
Here my command (example):
metan log_aOR log_aOR_lci log_aOR_uci if Outcome_regrouped_3==1 & Test_Final=="Diabetes", npts(N) random eform effect(adjusted OR) sortby (Year) forestplot(lcols (Study Year) favours (Risk reduced # Risk increased) fp(2) xlabel (0.7 1 2 4) astext (80) boxscale (100) diamopts (fcolor(dknavy)) leftjustify maxlines(1) null(1) nlineopts(lcolor(navy) lwidth(medthin) lpattern(dash_dot)) hlineopts(lpattern(solid)))
Question 1) Any suggestion on how to add the 95%CI of I^2 to the forest plots?
Question 2) cf. the command above:
For the outcome == 1 and the exposure "Diabetes" I have 4 studies in my dataset, D-L pooled OR=1.34 (95CI 1.06-1.69), I^2= 74.6%, p<.0001. Here is the metan output for heterogeneity:
---------------------------------------------------------
Measure | Value df p-value
---------------------+-----------------------------------
Cochran's Q | 23.61 6 0.001
| -[95% Conf. Interval]-
H | 1.984 1.000 3.581
I² (%) | 74.6% 0.0% 92.2%
---------------------------------------------------------
When I use the heterogi command to recalculate these confidence intervals, I get different results for the 95%CI.
heterogi 23.61 6
------------------------------------------------------
Statistic | Estimate [95% Conf. Interval]
----------+-------------------------------------------
H | 2.0 1.4 2.9
I^2 | 75 46 88
------------------------------------------------------
Why? seems to me that I am misinterpreting the I^2 95%CI of the metan command or are the CIs calculated with a different method?
Thanks in advance
Peter
I am performing several meta-analyses using the metan command in STATA v 18.0 for a systematic review ob observational studies focusing on prognostic factors. As most meta-analyses include 3-6 studies, the I^2 point estimate is unreliable, so that I wanted to include the confidence intervals of I^2 into the forestplots rather than (or in addition to) the p-values.
Here my command (example):
metan log_aOR log_aOR_lci log_aOR_uci if Outcome_regrouped_3==1 & Test_Final=="Diabetes", npts(N) random eform effect(adjusted OR) sortby (Year) forestplot(lcols (Study Year) favours (Risk reduced # Risk increased) fp(2) xlabel (0.7 1 2 4) astext (80) boxscale (100) diamopts (fcolor(dknavy)) leftjustify maxlines(1) null(1) nlineopts(lcolor(navy) lwidth(medthin) lpattern(dash_dot)) hlineopts(lpattern(solid)))
Question 1) Any suggestion on how to add the 95%CI of I^2 to the forest plots?
Question 2) cf. the command above:
For the outcome == 1 and the exposure "Diabetes" I have 4 studies in my dataset, D-L pooled OR=1.34 (95CI 1.06-1.69), I^2= 74.6%, p<.0001. Here is the metan output for heterogeneity:
---------------------------------------------------------
Measure | Value df p-value
---------------------+-----------------------------------
Cochran's Q | 23.61 6 0.001
| -[95% Conf. Interval]-
H | 1.984 1.000 3.581
I² (%) | 74.6% 0.0% 92.2%
---------------------------------------------------------
When I use the heterogi command to recalculate these confidence intervals, I get different results for the 95%CI.
heterogi 23.61 6
------------------------------------------------------
Statistic | Estimate [95% Conf. Interval]
----------+-------------------------------------------
H | 2.0 1.4 2.9
I^2 | 75 46 88
------------------------------------------------------
Why? seems to me that I am misinterpreting the I^2 95%CI of the metan command or are the CIs calculated with a different method?
Thanks in advance
Peter
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