Making sense of effect size in meta-analysis based for medical research - Pubrica
                     MAKING SENSE OF 
EFFECT  SIZE IN META-
ANALYSIS  BASED FOR 
MEDICAL  RESEARCH
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Pubrica 
 Group: www.pubrica.com
Email: [email protected]
Today's Discussion
Outline
Introduction
Cohen's D Effect 
Significance of Effect Size
Size  Effect Size in Fixed Effects Model 
Meta-Analysis   Random Effects 
Formulation for Effect Model  Future 
Size Scopes
Standardized Means 
Difference
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.
Contd...
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.
Contd...
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.
Contd...

Significance  
of Effect Formulae for evaluating the effect sizes do not often  
Size 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.
Effect Size 
In Meta-analysis, the effect size is concerned about   in Meta-  
various studies and afterwards joins all the studies into 
 a single analysis. Analysis
In statistical analysis, the effect size is typically  
estimated in three ways:
(1) The standardized mean difference,
(2) Odd ratio,
(3) Correlation coefficient.
Contd...

Formulation  
for Effect 
Size Karl
Pearson
created
Pearson r
correlation,  and it is most broadly utilized in 
statistics.
This parameter of effect size is signified by r.
The estimation of the effect size of Pearson r 
 connection shifts between -1 to +1.
Contd...
Contd...
Where
r = correlation coefficient ∑y = sum of y scores
N = number of pairs of scores ∑x2= sum of squared x scores
∑XY = sum of the products of paired scores ∑y2= sum of squared y scores
∑x = sum of x scores
Standardized 
 Means  When a research study depends on the population mean  
Difference and standard deviation, at that point, the accompanying  
technique is utilized to know the effect size:
Cohen's D 
 Effect Cohen's d is known as the distinction of two population  
means, and the standard deviation separates it from 
Size  the data.
Mathematically Cohen's effect size is signified by:
Contd...
Where s can be calculated by using the following formula:
Contd...
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:
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.
Contd...
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.
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:
Contd...
Step 1: Inverse variance weighting.
Step 2: Un-weighting of inverse variance weighting by REVC (Random
Effects  Variance Component).
Future 
 The more significant variability in effect size e (also
called  heterogeneity) is the more prominent in un-weighting.
Scopes
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).
Contact Us
UNITED KINGDOM
+44- 7424810299
INDIA
+91-9884350006
EMAIL
[email protected]