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

Michael Dewey ||@t@ @end|ng |rom dewey@myzen@co@uk
Sun May 24 12:25:11 CEST 2020

Dear Nelly

Comments in-line

On 23/05/2020 16:27, ne gic wrote:
> 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) -
> manually calculated instead.
> 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?

But you said you do not have the n necessary to do this so it is not 
going to help I think.

>     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?

The two most used R packages are meta and metafor either of which will 
do what you want.

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

I think you are going to have to restrict yourself to those studies 
which do give the number at risk at 2 years. I must say I would be 
rather nervous about doing this if the degree of and reasons for 
censoring were likely to be different between studies.


> 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]]
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>> --
>> 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]]
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