[R-meta] Fwd:

[Im]

Hello!
Thanks for your reply.

Ok allright, then we can consider the HR values for meta analysis. P value
for overall effect could be reported, I suppose.

best regards,
Imran

On Wed, Oct 6, 2021 at 3:31 PM Michael Dewey <lists using dewey.myzen.co.uk>
wrote:

> If you have the effect sizes (the HR) then what scientific question are
> you hoping to answer by also analysing the p-values? Normally that is a
> last resort when you do not have effect sizes.
>
> Michael
>
> On 06/10/2021 10:03, Im wrote:
> >
> > Dear All!
> > Thanks a lot everyone for helping me to conduct the meta analyses and
> > making me reach to results stage of the manuscript.
> >
> > However, I still have some crucial questions regarding interpretation of
> > meta-analysis with HR values, alongwith meta-analysis of their
> > respective P-values.
> > *
> > *
> >
> > *Data Collection:*
> > *
> > *
> > Mainly, I have gathered *2-4 P values each of Hazard Ratios (HR values
> > given with CIs) from studies on* *specific* *prognostic markers
> > reporting* *DFS, OS and DSS*.
> >
> >
> > *_R Studio Codes for meta-analysis of HR values:_*
> > *
> > *
> > *library(meta)*
> > *
> > settings.meta(digits = 2, layout = "RevMan5")
> > *
> > *
> > *
> > *HR <- c(1.34,1.95) */(Hypothetical values)/
> > *
> > lower.HR <- c(1.10,0.75) *
> > *
> > upper.HR <- c(2.34,2.25) *
> > *
> > study<- c("ABC et al. 2018","EFG et al. 2020")*
> > *
> >
> m2<-metagen(log(HR),lower=log(lower.HR),upper=log(upper.HR),studlab=study,sm="HR")*
> > *
> > m2*
> > *
> > forest(m2)*
> > *
> > grid::grid.text("Meta-analysis of Prognostic marker for Multivariate
> DFS",
> >                  0.5, 0.94, gp = grid::gpar(cex = 1.5)) *
> >
> >
> > *_R Studio Codes for meta-analysis of P values:_*
> > *
> > *
> >
> > *install.packages("BiocManager")*
> >
> > *BiocManager::install("multtest")*
> >
> > *install.packages("metap")*
> >
> > **
> >
> > *library(metap)*
> >
> > *pvals <- c(0.08,0.3) */(Hypothetical values)/
> >
> > *//*
> >
> > *result <- sumlog(pvals)*
> >
> > **
> >
> > *result*
> >
> > *
> > *
> >
> > *_Results of Meta-analysis with P values:_*
> > *
> > *
> >
> > 1. chisq = 16.66974  with df =  4  p =  0.002240369
> >
> >
> > 2. chisq =  35.80921 with df =  8  p = 1.902998e-05   (**Do we report
> > this result as 0.000019 in the manuscript ?)
> >
> >
> >
> > *_Questions:_*
> >
> > *
> > *
> >
> > 1.Please view attached first image of forest plot which I have got from
> > "Meta" Package in RStudio. The P value in this forest plot is for
> > heterogeneity, but the P value for overall effect size is not there as
> > presented in the second attached image as: *(Test or overall effect
> > z=0.35, /p/=0.7)*. Should I, and if "yes" then how to report this P
> > value in my forest plot ?
> >
> > And is this P value similar to the P value which I am getting from
> > meta-analysis of P values with "metap" package ? which P value to be
> > reported ideally and usually ?
> >
> >
> > 2. Regarding *"metap*" package my cumulative P values are too low, so
> > how to interpret these results. Is there no image OR forest plot for
> > them to report ? Does the significance level (0.005) changes for
> > cumulative P values ?
> >
> >
> > 3. Can I report these cumulative P value meta-analysis results along
> > with regular meta-analysis of cumulative HR values (forest plots) ?
> >
> >
> > I will be much grateful for your much valuable expert advice to finalize
> > my results.
> > best regards,
> > Imran
> >
> > <
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> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>

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