[R-meta] Subgroups using metaprop

Dr. Gerta Rücker gert@@ruecker @end|ng |rom un|k||n|k-|re|burg@de
Sat Nov 15 15:53:28 CET 2025 

Hi Lindsay,
Just tried the same without noticing Michael's response (he was faster). The code runs smoothly with R version 4.5.1, meta version meta_8.2-1. Below you find the result. I would remove the common effect model in a subgroup analysis.

Best,
Gerta

        proportion           95%-CI %W(common) %W(random) Intensity
Study A     0.0740 [0.0720; 0.0761]       27.3       10.0       Low
Study B     0.1835 [0.1714; 0.1960]        3.6       10.0  Moderate
Study C     0.1275 [0.1233; 0.1318]       16.4       10.0  Moderate
Study D     0.1590 [0.1526; 0.1656]       10.2       10.0  Moderate
Study E     0.1496 [0.1404; 0.1591]        4.5       10.0  Moderate
Study F     0.2985 [0.2915; 0.3055]       21.4       10.0  Moderate
Study G     0.1893 [0.1706; 0.2091]        1.6        9.9  Moderate
Study H     0.2130 [0.2038; 0.2223]        7.9       10.0      High
Study I     0.2686 [0.2561; 0.2814]        5.9       10.0  Moderate
Study J     0.0714 [0.0626; 0.0811]        1.3        9.9      High

Number of studies: k = 10
Number of observations: o = 143776
Number of events: e = 19698

                     proportion           95%-CI
Common effect model      0.1535 [0.1515; 0.1555]
Random effects model     0.1608 [0.1187; 0.2143]

Quantifying heterogeneity (with 95%-CIs):
 tau^2 = 0.3226 [0.1518; 1.0801]; tau = 0.5680 [0.3896; 1.0393]
 I^2 = 99.9% [99.8%; 99.9%]; H = 26.82 [25.28; 28.44]

Test of heterogeneity:
       Q d.f. p-value
 6471.43    9       0

Results for subgroups (common effect model):
                       k proportion           95%-CI       Q   I^2
Intensity = Low        1     0.0740 [0.0720; 0.0761]    0.00    --
Intensity = Moderate   7     0.1998 [0.1967; 0.2029] 2104.62 99.7%
Intensity = High       2     0.1853 [0.1777; 0.1931]  279.96 99.6%

Test for subgroup differences (common effect model):
                     Q d.f. p-value
Between groups 4086.85    2       0
Within groups  2384.58    7       0

Results for subgroups (random effects model):
                       k proportion           95%-CI  tau^2    tau
Intensity = Low        1     0.0740 [0.0720; 0.0761]     --     --
Intensity = Moderate   7     0.1904 [0.1497; 0.2392] 0.1519 0.3897
Intensity = High       2     0.1262 [0.0404; 0.3315] 0.7887 0.8881

Test for subgroup differences (random effects model):
                   Q d.f.  p-value
Between groups 53.53    2 < 0.0001

Details of meta-analysis methods:
- Inverse variance method
- Restricted maximum-likelihood estimator for tau^2
- Q-Profile method for confidence interval of tau^2 and tau
- Calculation of I^2 based on Q
- Logit transformation
- Clopper-Pearson confidence interval for individual studies




UNIVERSITÄTSKLINIKUM FREIBURG
Institute for Medical Biometry and Statistics

Dr. Gerta Rücker
Guest Scientist

Stefan-Meier-Straße 26 · 79104 Freiburg
gerta.ruecker using uniklinik-freiburg.de

https://www.uniklinik-freiburg.de/imbi-en/employees.html?imbiuser=ruecker

-----Ursprüngliche Nachricht-----
Von: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> Im Auftrag von Michael Dewey via R-sig-meta-analysis
Gesendet: Samstag, 15. November 2025 12:13
An: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis using r-project.org>
Cc: Michael Dewey <lists using dewey.myzen.co.uk>; Lindsay Gaudet <lovstrom using ualberta.ca>
Betreff: Re: [R-meta] Subgroups using metaprop

Dear Lindsay

When I run your code here it works seamlessly. Do you perhaps have old 
versions of one or other of the packages mentioned? Or are you using an 
incompatible version of R? I would suggest doing update.packages() to 
see if that clears it. If you have an old version of R I would update 
that first.

Michael

On 14/11/2025 23:52, Lindsay Gaudet via R-sig-meta-analysis wrote:
> I am trying to do a meta-analysis of proportions using the inverse
> variance method. Attempting subgroup analyses with 3 subgroups,
> regardless of the method to estimate tau or to estimate fixed (common)
> or random effects, returns an error. The error occurs regardless of
> the number of studies in each group, and regardless of the analysis
> method chosen (i.e., other transformations, or attempting GLMM or
> different methods to estimate tau). It does not occur when there are
> only 2 subgroups.
> 
> The error that keeps coming up says: "Error in .C("ruben", lambda =
> as.double(lambda), h = as.integer(h), delta = as.double(delta),  :
>    "ruben" not available for .C() for package "CompQuadForm""
> 
> My code is below:
> 
> library (meta)
> data <- structure(list(Study = c("Study A", "Study B", "Study C",
> "Study D", "Study E", "Study F", "Study G", "Study H", "Study I",
> "Study J" ), Intensity = c("Low", "Moderate", "Moderate", "Moderate",
> "Moderate", "Moderate", "Moderate", "High", "Moderate", "High"),
> events = c(4769, 717, 3031, 1950, 857, 4925, 310, 1626, 1292, 221), n
> = c(64425, 3908, 23771, 12264, 5729, 16501, 1638, 7635, 4810, 3095)),
> class = "data.frame", row.names = c(NA, -10L))
> mp <- metaprop(events, n,
>       data = data,
>       method = "INVERSE",
>       sm="PLOGIT",
>       studlab = Study,
>       subgroup = Intensity)
> 
> I would be very grateful if anyone has any insight into what is going on here.
> 
> Lindsay
> 
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-- 
Michael Dewey

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