[R-meta] Dear Wolfgang

Michael Dewey ||@t@ @end|ng |rom dewey@myzen@co@uk
Wed Apr 1 14:28:03 CEST 2020

Dear Ju

Based on that I would definitely include a moderator to differentiate 
short term from long term.


On 31/03/2020 21:44, Ju Lee wrote:
> Dear Wolfgang and all,
> Thank you very much for these different suggestions. If time-averaging 
> effect sizes are less straightforward in our case, we may  lean more 
> toward using a single final data point, based on the logic that all of 
> the responses measured were accumulative to more general extent. We also 
> considered standardizing this by using the first sampling (as Gerta 
> suggested) instead, but these multiple time-point studies were conducted 
> for a reason as in their particular study systems and responses being 
> measured, it takes naturally longer to see treatment effects compared to 
> ones used in short-term studies. Maybe it short vs. long term 
> measurement as a moderator could be a supplementary moderator to see if 
> that drove any response variations?
> Wolfgang: I have looked into the chapter that you have suggested in 
> Gleser and Olkin (2009), and I am less clear from reading the section 
> related to how you can assume the correlation estimate between Y_j and 
> Y_j* in equation 19.26 and 27. Do you have any suggestions on relatively 
> straightforward ways to calculate or estimate sample correlation from 
> different means of same treatment or control groups across the multiple 
> sampling time point?
> In this section of Gleser and Olkin (2009), they say: "When the sample 
> covariance matrix of the endpoint measures for the study is published, 
> ˆjj* can be taken to be the sample correlation, rjj*. Otherwise, ˆjj* 
> will have to be imputed from other available information, for example, 
> from the sample correlation for endpoints Y_j and Y_j* taken from 
> another study."
> Thank you everyone for your time and inputs!
> Sincerely,
> JU
> ------------------------------------------------------------------------
> *From:* Michael Dewey <lists using dewey.myzen.co.uk>
> *Sent:* Tuesday, March 31, 2020 4:28 AM
> *To:* Dr. Gerta Rücker <ruecker using imbi.uni-freiburg.de>; Nicky Welton 
> <Nicky.Welton using bristol.ac.uk>; 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
> 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
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