-----Original Message-----
From: ne gic [mailto:negic4 at gmail.com]
Sent: Thursday, 28 May, 2020 13:02
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] Meta-Analysis: Proportion in overall survival rate
Dear?Wolfgang,
A quick follow up. After meta-analyzing the proportions using a logit
transformation using qlogis(p), how I can back transform the proportion to
fit the normal range as I get some values below 0 on the forest plot when I
directly use the rma object.
forest(pes.da_30plus, xlab = "2-year survival (%)")
Sincerely,
nelly
On Wed, May 27, 2020 at 8:17 PM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Dear Nelly,
Your equation for the SE assumes that the p behaves like a 'regular'
proportion computed from a binomial distribution. I am not sure if this is
correct when using the Kaplan-Meier estimator to derive such a proportion.
As far as your input to rma() is concerned - that is correct. However, I
would consider not meta-analyzing the proportions directly, but doing a
logit transformation on p, so using qlogis(p) for yi and sqrt(1/(p*n) +
1/((1-p)*n)) for the SE.
Best,
Wolfgang
-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
On Behalf Of ne gic
Sent: Wednesday, 27 May, 2020 20:02
To: Dr. Gerta R?cker
Cc: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] Meta-Analysis: Proportion in overall survival rate
Dear Michael, Gerta and List,
I would like to cross-check with you what I have done.
I have restricted myself to Kaplan-Meier studies which gave the number at
risk at 2 years, and also n_0 at baseline.
I then estimated the absolute number of those surviving as *n_t *= n_0*S(t)
following Gerta's idea. I took the reported proportions at 2 years to
represent the S(t).
I calculated the standard error (SE) using the formula: *se *= square root
*p*(1-*p*)/n). Where *p* = proportion at 2 years i.e. S(t)
, n = *n_t*, the estimated number of of those surviving.
I then used the random effects model in metafor as follows:
rma(yi = *p*, sei = *se*, data=mydata, method="REML")
The resulting estimate seems reasonable to me. But I want to confirm with
you if this is the way one would input SE and the proportion to the
function.
Welcome any comments.
Sincerely,
nelly