[R-meta] Question about a meta-analysis of 2 studies
Michael Dewey
||@t@ @end|ng |rom dewey@myzen@co@uk
Thu Nov 14 16:53:33 CET 2024
Dear Adelina
Comment in-line
On 14/11/2024 13:57, Adelina Artenie wrote:
> Hi Michael,
>
> Thanks for the reply. In my code, I referenced the paper which
> recommends this (counter-intuitive) approach: appendix 3 (I can’t seem
> to be able to attach here).
>
> There are different ways of implementing the same method. For example,
> we could also do:
>
> meta_inci <- metagen(TE = ln_inci,
>
> lower = ln_LB,
>
> upper = ln_UB,
>
> studlab = idd_count,
>
> data = df_inci,
>
> sm = "IRLN",
>
> method.tau ="SJ" ,
>
> comb.fixed = FALSE,
>
> comb.random = TRUE, backtransf = TRUE,
>
> hakn = TRUE,
>
> text.random = "Overall")
>
> summary(meta_inci)
>
> Both approaches produce the same results, so it does not seem to be a
> coding problem.
>
> Agree the variance is expected to be large but the estimated 95%CI are
> unrealistic (0 - >1000).
No. It is perfectly realistic. It is not what you wanted but it reflects
the lack of precision here.
Michael
>
> Adelina
>
> *From: *R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org>
> on behalf of Michael Dewey via R-sig-meta-analysis
> <r-sig-meta-analysis using r-project.org>
> *Date: *Thursday, 14 November 2024 at 13:42
> *To: *Adelina Artenie via R-sig-meta-analysis
> <r-sig-meta-analysis using r-project.org>
> *Cc: *Michael Dewey <lists using dewey.myzen.co.uk>
> *Subject: *Re: [R-meta] Question about a meta-analysis of 2 studies
>
> Dear Adelina
>
> You state that you are interested in the HKSG method but I do not see an
> exampe of that in your code. You are also doing something which metafor
> regards as incompatible (knha with FE).
>
> But the main problem is that you are trying to estimate a variance
> (tau^2) based on only two observations. This is in general very imprecise.
>
> If you can clarify what your underlying scientific goal is it may be
> that somebody, quite likely not me< can offer a way forward.
>
> Michael
>
>
> On 14/11/2024 11:11, Adelina Artenie via R-sig-meta-analysis wrote:
>> Hello,
>>
>> The HKSG<https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-25#MOESM1 <https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-25#MOESM1>> approach has been proposed to be used when the number of studies to pool is small, instead of more traditional meta-analysis methods.
>> I have to pool several estimates in cases where there are only 2 estimates, often quite different from each other and with varying levels of precision.
>> In pretty much all cases, the HKSG method seems to break down, leading to unrealistic 95%CI (this seems to improve as soon as I have at least 3 estimates and gets better with more estimates).
>> Conceptually, I get it: we have only 2 studies and the estimates are very different, so a meta-analysis is not ideal. But if I still want to do it, do you know of other methods that could better account for heterogeneity than traditional methods, even if imperfect?
>> I included some example code below.
>> Thanks
>> Adelina
>>
>>
>> library(meta)
>> library(metafor)
>>
>> idd_count <- c(1, 2)
>>
>> inci <- c(11.1849, 1.484536956)
>> CI95_LB <- c(6.8522, 1.042335486)
>> CI95_UB <- c(18.2571, 1.985159973)
>> df_inci <- data.frame(idd_count, inci, CI95_LB, CI95_UB)
>>
>> # DL estimator for tau
>> df_inci$ln_inci <- log(df_inci$inci)
>> df_inci$ln_LB <-log(df_inci$CI95_LB)
>> df_inci$ln_UB <-log(df_inci$CI95_UB)
>>
>> meta_inci <- metagen(TE = ln_inci,
>> lower = ln_LB,
>> upper = ln_UB,
>> studlab = idd_count,
>> data = df_inci,
>> sm = "IRLN",
>> method.tau = "DL", # switching between estimators (eg, REML, PM) gives the same results
>> comb.fixed = FALSE,
>> comb.random = TRUE, backtransf = TRUE,
>> text.random = "Overall")
>> summary(meta_inci)
>>
>>
>> # HKSJ approach: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-25#MOESM1 <https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-25#MOESM1>
>>
>> df_inci$ln_SE <- (df_inci$ln_inci - df_inci$ln_LB) / 1.96
>>
>> meta_modellll <- rma.uni(yi = ln_inci,
>> sei = ln_SE,
>> method = "FE", # intentionally set as FE, following recommendations by Inthout et al 2014
>> knha=TRUE,
>> data = df_inci)
>> summary(meta_modellll)
>>
>> point_estimate <- exp(meta_modellll$b)
>> lower_bound <- exp(meta_modellll$ci.lb)
>> upper_bound <- exp(meta_modellll$ci.ub)
>> cat("Point Estimate:", point_estimate, "\n")
>> cat("95% CI Lower Bound:", lower_bound, "\n")
>> cat("95% CI Upper Bound:", upper_bound, "\n")
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-meta-analysis mailing list @ R-sig-meta-analysis using r-project.org
>> To manage your subscription to this mailing list, go to:
>> https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis
> <https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis>
>>
>
> --
> Michael
>
> _______________________________________________
> R-sig-meta-analysis mailing list @ R-sig-meta-analysis using r-project.org
> To manage your subscription to this mailing list, go to:
> https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis
> <https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis>
>
>
> <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> Virus-free.www.avg.com <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient>
>
> <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>
--
Michael
More information about the R-sig-meta-analysis
mailing list