[R-meta] Dear Wolfgang

[Michael Dewey]

Indeed if the single time point studies have used different times Ju 
will probably want to do a meta-regression with time as a moderator so 
it would not matter too much if Ju chose any single value from the 
multiple time point studies. That would avoid the complexity of 
estimating the V matrix.

Michael

On 30/03/2020 19:42, Dr. Gerta Rücker wrote:
> Dear Ju,
> 
> Another (maybe simplistic) solution could be to use only one (e.g., 
> always the first) time point for those studies that report repeated 
> measurements. This can be justified because you wrote that the "large 
> majority of studies measure this over short term experiment and thus on 
> a single time point" - so, as I understand, you it is only a minority of 
> studies that causes the multiplicity issue.
> 
> A similar simplification is often done when including a small number of 
> cross-over trials into a meta-analysis of RCTs with parallel-group design.
> 
> Best,
> 
> Gerta
> 
> Am 30.03.2020 um 20:36 schrieb Nicky Welton:
>> If you are interested in modelling the time-course relationship then 
>> there is a new package in R to do this within a network meta-analysis 
>> framework (although it can also be used if there are only 2 
>> interventions):
>> https://cran.r-project.org/web/packages/MBNMAtime/index.html
>>
>> Best wishes,
>>
>> Nicky
>>
>> -----Original Message-----
>> From: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> 
>> On Behalf Of Viechtbauer, Wolfgang (SP)
>> Sent: 30 March 2020 19:00
>> To: Ju Lee <juhyung2 using stanford.edu>; r-sig-meta-analysis using r-project.org
>> Subject: Re: [R-meta] Dear Wolfgang
>>
>> Thanks for the clarification.
>>
>> Computing a time-averaged d (or g) value is tricky because the values 
>> are not independent. So, if you meta-analyze them, the standard error 
>> of the pooled estimate would not be correct unless you take the 
>> dependency into consideration. And to answer one of your questions, 
>> squaring the standard error from the model would give you the sampling 
>> variance, but again, that value would not be correct.
>>
>> Basically what you have is the 'multiple-endpoint' case described here:
>>
>> http://www.metafor-project.org/doku.php/analyses:gleser2009#multiple-endpoint_studies 
>>
>>
>> You would need an estimate of the correlation between the repeated 
>> measurements (and if you have more than two time points, then an 
>> entire correlation matrix) to construct the V matrix for each study 
>> before meta-analyzing the values. Then you could use:
>>
>> res <- rma.mv(yi, V, data=dat)
>>
>> to pool the estimates into a time-averaged estimate, coef(res), and 
>> vcov(res) would give you the sampling variance. But the difficult part 
>> is constructing V.
>>
>> Maybe you can make some reasonable assumptions about the size of 
>> correlation (which probably should be lower the further time points 
>> are separated, although if there are seasonal effects, then 
>> measurements taken during similar seasons - even if they are further 
>> apart - may tend to be more correlated again). Based on equations 
>> (19.26) and (19.27) from the Gleser and Olkin  (2009) chapter, you can 
>> then construct the V matrix.
>>
>> Best,
>> Wolfgang
>>
>> -----Original Message-----
>> From: Ju Lee [mailto:juhyung2 using stanford.edu]
>> Sent: Monday, 30 March, 2020 18:16
>> To: Michael Dewey; Viechtbauer, Wolfgang (SP); 
>> r-sig-meta-analysis using r-project.org
>> Subject: Re: [R-meta] Dear Wolfgang
>>
>> Dear Wolfgang, Michael
>> These questions are important, and thank you for pointing them out.
>> In answers to your questions:
>> 1. Studies are measuring predation or herbivory rate on experimental 
>> prey in control and treatment conditions in the field. Large majority 
>> of studies measure this over short term experiment and thus on a 
>> single time point (ex. After 24 hr of field exposure).
>>
>> 2. However, some studies will monitor these responses over long-term 
>> period over multiple seasons to understand the seasonal dynamics. The 
>> issue here is that responses show seasonal fluctuation, which is what 
>> they were looking for. But the reviewer of our study has warned 
>> against using single representative timepoint within these multiple 
>> measures (ex. Peak season) but rather time-average these multiple 
>> measurements to reduce time-related bias.
>>
>> 3. So back to the point: In all of studies, in multiple time point, 
>> same treatment vs. control group are being compared using the same 
>> sampling method. We have mean and SD for all of these data point, 
>> separately for control and treatment groups for each time point of 
>> multiple measurements.
>>
>> 4. I am using Hedges' d as effect sizes.
>> Thank you and I hope this clarifies the question better!
>> Best,
>> JU
>>
>> ________________________________________
>> From: Michael Dewey <lists using dewey.myzen.co.uk>
>> Sent: Monday, March 30, 2020 5:32 AM
>> To: Viechtbauer, Wolfgang (SP) 
>> <wolfgang.viechtbauer using maastrichtuniversity.nl>; Ju Lee 
>> <juhyung2 using stanford.edu>; r-sig-meta-analysis using r-project.org 
>> <r-sig-meta-analysis using r-project.org>
>> Subject: Re: [R-meta] Dear Wolfgang
>> And in addition to Wolfgang's comments it would be helpful to know 
>> what scientific question underlies the decision to measure at multiple 
>> time points. Presumably the authors of primary studies did not do it 
>> for fun.
>>
>> Michael
>>
>> On 30/03/2020 11:37, Viechtbauer, Wolfgang (SP) wrote:
>>> Dear Ju,
>>>
>>> Before I can try to address your actual questions, please say a bit 
>>> more about the studies that measure responses at a single time point. 
>>> Are groups (e.g., treatment versus control) being compared within 
>>> these studies? Are the 'responses' continuous (such that means and 
>>> SDs are being reported) or dichotomous (such that counts or 
>>> proportions are being reported) or something else? And related to 
>>> this, what effect size measure are you using for quantifying the 
>>> group difference within studies? Standardized mean differences (which 
>>> would make sense when means/SDs are being reported), risk differences 
>>> or (log) risk/odds ratios (based on counts/proportions), or something 
>>> else?
>>>
>>> And the studies that measure responses at multiple time points: Are 
>>> they just doing the same thing that the 'single time point studies' 
>>> are doing, but at multiple time points? For example, instead of 
>>> reporting the means and SDs of the treatment and control group once, 
>>> there are several follow-ups, such that such the means and SDs of the 
>>> two groups are reported at each follow-up time point?
>>>
>>> Best,
>>> Wolfgang
>>>
>>> -----Original Message-----
>>> From: R-sig-meta-analysis
>>> [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Ju Lee
>>> Sent: Sunday, 29 March, 2020 20:16
>>> To: r-sig-meta-analysis using r-project.org
>>> Subject: [R-meta] Dear Wolfgang
>>>
>>> Dear Wolfgang,
>>>
>>> I sincerely hope you are well and healthy.
>>> I wanted to reach out regarding ways to incorporate studies with 
>>> repeated-measures to overall mixed effect models.
>>>
>>> My data is almost entirely composed of studies measuring responses at 
>>> a single time point, but there are few studies that have been 
>>> measuring responses multiple times throughout study seasons. I was 
>>> advised that time-averaging these multiple responses makes more sense 
>>> for these studies.
>>>
>>> My understanding was that you could 1) do a fixed effect 
>>> meta-analysis of these studies to generate a single mean effect sizes 
>>> and sampling variance from these repeated measurements and then 2) 
>>> incorporate the single effect size and variance into overall 
>>> mixed-effect model. Is this a correct approach?
>>>
>>> If so, how would I calculate sampling variance from the fixed model 
>>> in the step 1? Is it based on SE outputs of the fixed effect model?
>>>
>>> Thank you very much, and I look forward to hearing from you!
>>> Best,
>>> JU
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-- 
Michael
http://www.dewey.myzen.co.uk/home.html