# How to perform meta-analysis comparing different proportional mortality ratios of subgroups?

I am working on a meta-analysis of observational studies (basically 39 retrospective papers on different surgical interventions for one condition). I've obtained different weighted proportions (random effects model) through MedCalc for both the full data set and the different subgroups. So far I have only found information on meta-analyses for randomized controlled trials, which I cannot apply to my data since I lack the "control group" in my retrospective papers.

• What is the best statistic method and/or software to use for single-arm, uncontrolled, retrospective studies?
• How I can compare the different proportional mortality ratios of my subgroups?
• When you say 'observational studies', are you referring to single arm, uncontrolled, studies or controlled studies? Jun 25, 2013 at 17:12
• Single arm, uncontrolled, retrospective. Jun 25, 2013 at 18:28

I personally use Comprehensive Meta-Analysis, but if you can calculate the SE for each study then you could use RevMan.

Below I put the results for four fictional studies grouped into two groups:

Groups          Effect size and 95% interval                Test of null (2-Tail)           Heterogeneity                   Tau-squared Tau-squared Tau-squared Tau-squared
Group       Number Studies  Point estimate  Lower limit Upper limit     Z-value P-value     Q-value df (Q)  P-value I-squared       Tau Squared Standard Error  Variance    Tau

Fixed effect analysis

G1      2   7.03759108414197E-02    0.017603046885197   0.242332278757674       -3.51044333533216   4.47360144140374E-04        0.258038705280314   1   0.611470803514087   0       0   1.52999712888317    2.34089121439075    0
G2      2   2.88585870247873E-02    7.22732614865208E-03    0.10817723861972        -4.89941436298139   9.61227347051619E-07        4.26059028518016E-02    1   0.83646911862334    0       0   1.45672749793515    2.12205500324041    0
Total within                                        0.300644608132116   2   0.860430611444899
Total between                                       0.828447690689994   1   0.362721779796614
Overall     4   4.47962294435528E-02    1.68510891696442E-02    0.113723780117822       -5.95810589198247   2.55178167485326E-09        1.12909229882211    3   0.770056131706131   0       0   0.861629557762928   0.742405494810739   0

Mixed effects analysis  Mixed effects analysis  Mixed effects analysis  Mixed effects analysis  Mixed effects analysis  Mixed effects analysis  Mixed effects analysis  Mixed effects analysis  Mixed effects analysis

G1      2   7.03759108414197E-02    0.017603046885197   0.242332278757674       -3.51044333533216   4.47360144140374E-04
G2      2   2.88585870247873E-02    7.22732614865208E-03    0.10817723861972        -4.89941436298139   9.61227347051619E-07
Total within
Total between                                       0.828447690689994   1   0.362721779796614
Overall     4   4.47962294435528E-02    1.68510891696442E-02    0.113723780117822       -5.95810589198247   2.55178167485326E-09