[R-meta] Handling meta-analysis dataset with sampling variance equals zero
Tzlil Shushan
tz|||21092 @end|ng |rom gm@||@com
Tue Jan 11 02:32:02 CET 2022
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
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