# [R-meta] Meta-Analysis: Proportion in overall survival rate

ne gic neg|c4 @end|ng |rom gm@||@com
Sat May 23 17:27:57 CEST 2020

```Dear List and Gerta,

Once more am interested in overall survival and my aim is to analyse the
proportion(s) of patients left in the study at the 2 years time point as
reported by Kaplan-Meir (KM) curves. Of course there are those that are
censored and those that experience the event as time goes by as expected in
KM curves. I have now double checked all the studies to be included in my
meta-analysis dataset and I have selected all those that report the
proportion of patients left in the study at 2 years.
A number of those in the subset also included a risk table, thus I have
access to those at risk at this 2 year time point should I need them.

However, as I cannot directly infer the number of events(event) and total
at risk(n) from the curves at 2 years time point which would have been
convenient to plug into metaprop,
I thought that I could instead try Gerta's advice and see if I can use the
proportion (from each of the studies) and it's standard error (SE) -

Questions:

1. Is it correct to manually calculate the SE using the formula: SE =
square root (p(1-p)/n). Where p = proportion, n = total at risk?
2. Which R/Stata/SAS software function can then take in the proportion
and SE and give me a pooled proportion with CI and forest plot?

I welcome any comments and hints. If this is not reasonable, anything else
I can do?

Sincerely,
nelly

On Tue, May 19, 2020 at 2:32 PM Gerta Ruecker <ruecker using imbi.uni-freiburg.de>
wrote:

> Dear Nelly,
>
> You could do this, at least in principle, if all proportions refer to
> the same timepoint, for example 5 years. The problem is that the data
> you obtain from studies with a time-to-event endpoint are different from
> those that directly provide a five-year survival proportion: The
> time-to-event analysis accounts for censoring, while the proportion of
> living after five years relatively to all patients at baseline usually
> does not account for censoring or missing data (and thus may
> underestimate the true proportion).
>
> If I understand you correctly, you want to pool survival proportions
> (single-arm), not hazard ratios (comparing two arms).
>
> The technical thing is that you have survival proportions with standard
> error from the time-to-event studies and single proportions (survived/n)
> from other studies. Survival proportions with standard errors can be
> pooled usingthe  generic inverse variance method. Proportions are best
> be pooled using generalized linear models. See, for example, the
> examples for function metaprop() in R package meta.
>
> Best,
>
> Gerta
>
> Am 19.05.2020 um 14:15 schrieb ne gic:
> > Dear List,
> >
> >  From time to event data, it's common to calculate a combined HR for
> > instance from included studies - this I understand.
> >
> > Does it make sense to perform a meta-analysis of the proportion (%) one
> > gets from overall time survival e.g. Overall 5y survival? imagine a
> > scenario where different studies are reporting different proportions of
> > patients surviving at this time point and I want to report a summary
> > proportion from all the studies at this time point.
> >
> > If this is possible, does just collecting the proportion at that time
> point
> > e.g. 5 year suffice as the data to use for this calculation? Or what
> would
> > you suggest? Haven't seen a package that just takes a proportion.
> >
> > Sincerely,
> > nelly
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-meta-analysis mailing list
> > R-sig-meta-analysis using r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis
>
> --
>
> Dr. rer. nat. Gerta Rücker, Dipl.-Math.
>
> Institute of Medical Biometry and Statistics,
> Faculty of Medicine and Medical Center - University of Freiburg
>
> Stefan-Meier-Str. 26, D-79104 Freiburg, Germany
>
> Phone:    +49/761/203-6673
> Fax:      +49/761/203-6680
> Mail:     ruecker using imbi.uni-freiburg.de
> Homepage: https://www.uniklinik-freiburg.de/imbi.html
>
>

[[alternative HTML version deleted]]

```