[R-meta] Handling meta-analysis dataset with sampling variance equals zero

[James Pustejovsky]

Hi Tzlil,

I am trying to understand better what your outcomes are and what
questions you're trying to answer. From your explanation, it sounds
like you are interested in the distribution of speeds at which a
player runs during a game. So if you had the raw data for one player,
you might represent it as a histogram showing the amount of distance
traveled during a game (rescaled as distance traveled per minute of
game play) as a function of the speed:

Speed    Distance traveled (per minute of game play)
----------- ------------------------------------------------------------
26 km/h x
25 km/h xx
24 km/h x
23 km/h xxxx
22 km/h xxxx
21 km/h xxxxxx
20 km/h xxx
19 km/h xxxxxxxx
18 km/h xxxxxxxxxx
17 km/h xxxxxxxxxxx
16 km/h xxxxxxxxxxxx
15 km/h xxxxxxxxxxxxxxxx
14 km/h xxxxxxxxxxxxxxx
[etc.]

But from your explanation, it sounds like you only have summary
statistics on this distribution, such as histograms with much coarser
categories than what I have represented above. Is that correct? And if
so, do different studies generally use the same set of coarse speed
categories? Or does every study use different categories?

Also, I'm not clear about how you end up with a mean and a SD for each
of these buckets. Is the SD a summary over multiple individual
participants/players? Or over multiple repetitions for an individual?

All of the above questions are just about the outcomes you and your
colleagues are examining. How would you summarize your research
question? Is it about how variation in game format or game rules
affect the distribution of running speeds? So the "intervention" or
"treatment" of interest is a comparison of different game formats? If
that is correct, do the game formats vary within sample or only
between sample?

James


On Mon, Jan 10, 2022 at 7:32 PM Tzlil Shushan <tzlil21092 using gmail.com> wrote:
>
> Dear Wolfgang, James and the team..
>
> Me and colleagues are currently conducting a meta-analysis in the area of sports. In our analysis, we meta-analyse the exposure of high-speed running and sprinting in variations of games during football (soccer) training. For example, an outcome may be the distance covered (in meters) during a 4 versus 4 players game between 14.4 to 19.8 km.h only or the distance covered above 24.0 km.h. Hence, our main outcome is the mean and sampling variance of SD express as meters per minute game (we use "MN" in escalc function)..
>
> Considering that exposure to high velocity thresholds (e.g. >24 km.h) uncommonly happens during such games we have many outcomes that have mean and SD of 0 (we get sampling error of 0), also yielding to an overall estimate that is very close to zero. In other words, almost all of the distance covered during such games is in running speeds that are less than what is considered 'sprinting' in football.
>
> My main question is regarding heterogeneity..
>
> Whilst building the 'V-matrix' using the clubSandwich package we get a warning message of non-positive definite due to 0s in the matrix. We basically ignore this because it makes sense to have 0s as explained. Also, I know that when we run the model we don't get the Q-statistics and cannot calculate I^2 due to the same reason..
>
> I've been reading some of past discussions on these in the group however wanted to make sure and ask for reporting approaches. Is there any option to get these heterogeneity statistics with our data? Alternatively, can we basically state that we don't report these because of the nature of the dataset including many 0 values – and report tau only?
>
> The main thing is that we have a dataset including lower intensities which we obtain all aforementioned heterogeneity and we want to have a consistent report strategy in the paper, unless it is impossible due to the difference in outcomes across datasets..
>
> I appreciate your help here..
>
> Kind regards,
>
> Tzlil Shushan | Sport Scientist, Physical Preparation Coach
>
> BEd Physical Education and Exercise Science
> MSc Exercise Science - High Performance Sports: Strength & Conditioning, CSCS
> PhD Candidate Human Performance Science & Sports Analytics
>