Making sense of effect size in meta-analysis based for medical research – Pubrica
-
Upload
pubricahealthcare -
Category
Services
-
view
0 -
download
0
description
Transcript of Making sense of effect size in meta-analysis based for medical research – Pubrica
-
Copyright © 2021 pubrica. All rights reserved 1
Making Sense of Effect Size in Meta-Analysis
Based For Medical Research
Dr. Nancy Agnes, Head, Technical Operations, Pubrica, [email protected]
Keywords: Meta-analysis, fixed-effect model,
impacts meta-analysis, statistical analysis, cohen's d
effect size, random effects model
I. INTRODUCTION
Effect size is a statistical idea that helps measure the
strength and connection between two variables on a
numeric scale.
It simply refers to the size and the difference found
between the two groups. It's simple to compute,
understand, and apply to any educational or social
science outcome that can be quantified. It's especially
useful for calculating the efficiency of a certain
intervention concerning other interventions.
It is useful for calculating the efficiency of a certain
intervention in relation to other interventions. It
enables us to look further from the simple 'Does it
function or not?' question to "How well does it work
in a variety of contexts?" and significantly more
complex, by focusing on the most crucial feature of
an intervention. Rather than its statistical
significance, it promotes a different scientific
approach to the accumulation of knowledge. For
these reasons, the effect size is considered an
effective tool in reporting and interpreting
effectiveness.
For example, if we have data on the weight of men
and women and notice that, on average, men have
more weight than women, women's weight is known
as the effect size.
Statistical effect size helps us decide whether the
difference is genuine or a difference in factors.
II. SIGNIFICANCE OF EFFECT SIZE
Formulae for evaluating the effect sizes do not often
found in many statistics textbooks (other than those
devoted to meta-analysis), are not included in various
statistics computer packages and are occasionally
taught in standard research approaches courses. For
these above-stated reasons, even the researcher who
found interest in using measures of effect size is
afraid to use them in conventional practice and find it
quite hard to know exactly how to do it.
III. EFFECT SIZE IN META-ANALYSIS
In Meta-analysis, the effect size is concerned about
various studies and afterwards joins all the studies
into a single analysis.
In statistical analysis, the effect size is typically
estimated in three ways:
(1) The standardized mean difference,
(2) Odd ratio,
(3) Correlation coefficient.
IV. FORMULATION FOR EFFECT SIZE
Karl Pearson created Pearson r correlation, and it is
broadly utilized in statistics.[3] This parameter of
effect size is signified by r—the estimation of the
effect size of the Pearson r connection shifts is found
in-between -1 to +1.
mailto:[email protected]://pubrica.com/services/research-services/meta-analysis/
-
Copyright © 2021 pubrica. All rights reserved 2
Where
r = correlation coefficient
N = number of pairs of scores
∑XY = sum of the products of paired scores
∑x = sum of x scores
∑y = sum of y scores
∑x2= sum of squared x scores
∑y2= sum of squared y scores
V. STANDARDIZED MEANS DIFFERENCE
When a research study depends on the population
mean and standard deviation, at that point, the
accompanying technique is utilized to know the effect
size:
VI. COHEN'S D EFFECT SIZE
Cohen's d is known as the distinction of two
population means, and the standard deviation
separates it from the data.
Mathematically Cohen's effect size is signified by:
Where s can be calculated by using the following
formula:
Hedges' g method of effect size: This is the modified
form of Cohen's d method. We can write Hedges' g
method of effect size as follows:
VII. FIXED EFFECTS MODEL
The fixed-effect model gives a weighted average of a
progression of study estimates. The opposite of the
appraisals' difference is usually utilized as study
weight. More extensive studies will offer more than
smaller studies to the weighted average. Thus, when
concentrates inside a meta-analysis are overwhelmed
by an extensive study, the discoveries from smaller
studies are practically ignored.
This assumption is ordinarily unrealistic as an
examination is frequently inclined to several
heterogeneity sources; for example, treatment impacts
may contrast as indicated by region, measurements
levels, and study conditions.
VIII. RANDOM EFFECTS MODEL
A typical model used to synthesize heterogeneous
study is the irregular impacts model of meta-analysis.
This is the weighted average of the effect sizes of a
gathering of studies. The weight that is applied in this
interaction of weighted averaging with an arbitrary
impacts meta-investigation is accomplished in two
stages:
Step 1: Inverse variance weighting.
Step 2: Un-weighting of inverse variance weighting
by REVC (Random Effects Variance Component).
IX. FUTURE ENHANCEMENTS
The more significant variability in effect size e (also
called heterogeneity) is the more prominent in un-
weighting.
This can conclude that the arbitrary impacts meta-
analysis result turns out to be just the un-weighted
average effect size across the studies. At the other
limit, when all effect sizes are comparable (or
inconstancy doesn't surpass testing error), no REVC
is applied, and the irregular impacts meta-
examination defaults to just a fixed impact meta-
investigation (just opposite variance weighting).
REFERENCES
[1] Ens, D. (2013). Calculating and reporting effect
sizes to facilitate cumulative science: a practical
primer for t-tests and ANOVAs. Front.
Psychol. 4:863. doi: 10.3389/fpsyg.2013.00863
[2] Assen, M. A. L. M., van Aert, R. C. M., and
Wicherts, J. M. (2015). Meta-analysis using effect
size distributions of only statistically significant
studies. Psychol. Methods 20, 293–309. doi:
10.1037/met0000025
[3] Gavin Brupbacher, Heike Gerger, Thea Zander-
Schellenberg, Doris Straus, HildburgPorschke,
Markus Gerber, Roland vonKänel, Arno Schmidt-
Trucksäss, The effects of exercise on sleep in
unipolar depression: A systematic review and
network meta-analysis,Sleep Medicine Reviews,
(2021) https://doi.org/10.1016/j.smrv.2021.101452.
https://pubrica.com/academy/statistical/which-is-appropriate-to-use-fixed-effect-or-random-effect-statistical-model-while-conducting-meta-analyses/https://pubrica.com/academy/latest-topics/an-overview-of-fixed-effects-assumptions-for-meta-analysis/https://pubrica.com/academy/latest-topics/an-overview-of-fixed-effects-assumptions-for-meta-analysis/https://doi.org/10.1016/j.smrv.2021.101452
-
Copyright © 2021 pubrica. All rights reserved 3
[4] Behm, D.G., Alizadeh, S., Anvar, S.H. et al. Non-
local Acute Passive Stretching Effects on Range of
Motion in Healthy Adults: A Systematic Review with
Meta-analysis. Sports
Med (2021).https://doi.org/10.1007/s40279-020-
01422-5
[5] Nicholas Clarke, Lars PødenphantKiær, O.
JanneKjønaas, Teresa G. Bárcena, Lars Vesterdal,
Inge Stupak, LeenaFinér, Staffan Jacobson,
KęstutisArmolaitis, DagnijaLazdina, Helena Marta
Stefánsdóttir, Bjarni D. Sigurdsson,
[6] Effects of intensive biomass harvesting on forest
soils in the Nordic countries and the UK: A meta-
analysis,
[7] Forest Ecology and Management, (2021)
https://doi.org/10.1016/j.foreco.2020.118877.
https://doi.org/10.1007/s40279-020-01422-5https://doi.org/10.1007/s40279-020-01422-5https://doi.org/10.1016/j.foreco.2020.118877