[R-meta] Power analysis of meta-analysis

[Gerta Ruecker]

I'd like to strongly support Francesca and Ken.

Here's a blog you might enjoy: 
http://daniellakens.blogspot.com/2014/12/observed-power-and-what-to-do-if-your.html

Gerta

Am 18.06.2020 um 13:11 schrieb Ken Beath:
> Definitely, it seems to be required by reviewers who have decided that every paper must have a power analysis. Especially stupid for a meta-analysis, as what were they expecting, that someone would go out and find another study so there meta-analysis was sufficiently powered.
>
> Ken
>
>> On 18 Jun 2020, at 8:05 pm, CHAPPELL Francesca <F.Chappell using ed.ac.uk> wrote:
>>
>> There is a statistical literature against performing power calculations for existing datasets. Please see
>>
>> https://www.tandfonline.com/doi/pdf/10.1198/000313001300339897?needAccess=true  or https://journals.lww.com/annalsofsurgery/Fulltext/2019/01000/Don_t_Calculate_Post_hoc_Power_Using_Observed.46.aspx#O2-46-4 or
>> https://academic.oup.com/ndt/article/25/5/1388/1842407.
>>
>> This last one has a subsection on post hoc power calculations in the Section “Common Pitfalls”. I would gently point this out to the editors.
>>
>> Francesca
>>
>> -----Original Message-----
>> From: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> On Behalf Of Paul Chang
>> Sent: 18 June 2020 02:49
>> To: r-sig-meta-analysis using r-project.org
>> Subject: [R-meta] Power analysis of meta-analysis
>>
>> Dear list,
>>
>> I recently got the opportunity to revise my manuscript, which is a systematic review and prognostic meta-analysis. The effect estimates of the included studies are time-to-event data, i.e. ln(HR) and standard error of ln(HR). All the included studies are retrospective cohort studies, and the adjusted hazard ratios from multivariate cox regression model or from the propensity score matching cohort in the included studies were extracted for meta-analysis.
>>
>> The editors and reviewers commented that I cannot assume that either because all available data was included or because the total sample size is large that power was sufficient. They suggested that I should incorporate one of the following approach to justify the sample size.
>> 1. Perform power analysis: use a traditional sample size calculation to report the power with the given sample size to detect the difference considered to be clinically important (can assume independence of the observations across studies for the calculation) 2. Perform Trial sequential analysis (TSA)
>>
>> Due to the fact that the currently available TSA software developed by Copenhagen Trial Unit can only take care of continuous and binary data in the raw form, but not time-to-event data or any pre-calculated effect estimates, the first option seems to be the only solution. However, I'm not sure how to perform power analysis in meta-analysis. I've found a few websites to calculate power for meta-analysis, but they required the "effect size", which I assume is the Cohens' d from continuous data, and I have no idea how to convert the pooled hazard ratios to Cohens' d. Also, I'm aware that the powerEpi.default() function in "powerSurvEpi" package in R may be the solution. Yet, this function accounts for only two covariates but most of the hazard ratios I extracted were adjusted for more than two covariates in the multivariate Cox regression model. Moreover, some of the studies report only hazard ratios without reporting the event number, which is a required argument in the function. Finally, the function also requires to input the square of the correlation between the covariate of interest and the other covariate, which I certainly don't have.
>>
>> Can someone please give me some hints on how to solve this problem?
>> Thank you in advance and take care !
>>
>>
>>
>>
>>
>> --
>> 張君毓
>> Chang, Chun-Yu (Paul)
>> 慈濟大學醫學系100級
>> Class 2018, School of Medicine, Tzu Chi University
>> 台北慈濟醫院PGY
>> Post-graduate-year doctor, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei, Taiwan
>> E-mail: paulchang1231 using gmail.com <paulchan1231 using gmail.com>
>> Cell: 0978000933
>>
>> [[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
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