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Anova n metaanalysis
1. By
Dr Utpal Sharma
PG Student, Department of Community Medicine
Gauhati Medical College
ANOVA AND META-ANALYSIS
AN OVERVIEW
2. Introduction
Any data set has variability
Variability exists within groups…
and between groups
Question that ANOVA allows us to answer : Is
this variability significant, or merely by chance?
Sir R A Fisher
3. Definitions
ANOVA: analysis of variation in an experimental outcome
and especially of a statistical variance in order to determine
the contributions of given factors or variables to the variance.
Compares the means of groups of independent observations
ANOVA does not compare variances. We use variance-like
quantities to study the equality or non-equality of population
means.
Can compare more than two groups
Variance: the square of the standard deviation
4. Rationale of ANOVA
The ANOVA technique extends what an independent-samples
t test can do to multiple means.
If more than two means are compared, repeated t test will
lead to a higher Type I error rate.
A better approach is to consider all means in one null
hypothesis—that is, examining the plausibility of the null
hypothesis with a single statistical test.
Apart from saving time and energy, researchers can exercise
a better control of the probability of falsely declaring
significant differences among means.
5. Cont…
ANOVA or F test is associated with three
assumptions
Normal distribution
Variances of dependent variable are equal in all populations
Random samples; observations independently selected from
their respective populations.
σ2
1 =σ2
2 =σ2
3 =σ2
4 ……
6. One-Way ANOVA
The one-way analysis of variance is used to test the claim that
three or more population means are equal
The response/dependent variable is the variable we‘re
comparing
The factor/independent variable is the variable being used to
define the groups
The one-way is because each value is classified in exactly
one way
Examples include comparisons by gender, race, political party,
color, etc.
7. Cont…
For a sample containing K independent groups
ANOVA tests the null hypothesis which says that the means are all
equal
H0: μ1 = μ2 = … = μK
The alternative hypothesis is that at least one of the means is
different
H1: μi ≠ μj for some i, j
That is, “the group means are all equal”
The group means are not all equal
The ANOVA doesn’t test that one mean is less than another,
only whether they’re all equal or at least one is different.
8. Cont….
Variation
Variation is the sum of the squares (SS) of the deviations
between a value and the mean of the value
SST: The total variability of the dependent variable.
SSB: The variability between each group relative to the grand mean
SSW: The variability within each group relative to the group mean.
SST = (X - X)2 ; SST = SSB + SSW
SSB = NG (XG - X)2
SSW = (X1 - X1)2 + (X2 - X2)2 + …….. (Xk - Xk)2
Sum of Squares is abbreviated by SS and often followed by a
variable in parentheses such as SS(B) or SS(W) so we know
which sum of squares we’re talking about
9. Cont….
Degrees of Freedom, df
A degree of freedom occurs for each value that can vary before
the rest of the values are predetermined
The df is often one less than the number of values (N-1)
Variances (Mean of the Squares)
The variances abbreviated by MS, often with an accompanying
variable MS(B) or MS(W)
They are an average squared deviation from the mean and are
found by dividing the variation by the degrees of freedom
variation (SS)
Variance (MS)=
df
10. Cont..
F test statistic
An F test statistic is the ratio of two sample variances
The MS(B) and MS(W) are two sample variances and
that‘s what we divide to find F.
The F test statistic has an F distribution with df(B)
numerator df and df(W) denominator df
The p-value is the area to the right of the test statistic
F = MS(B) / MS(W)
11. ANOVA -Example
The statistics classroom is divided into three rows: front,
middle, and back
We want to see if the students further away did worse on the
exams
A random sample of the students in each row was taken
The score for those students on the second exam was
recorded
Front: 82, 83, 97, 93, 55, 67, 53
Middle: 83, 78, 68, 61, 77, 54, 69, 51, 63
Back: 38, 59, 55, 66, 45, 52, 52, 61
12. Cont….
The summary statistics for the grades of each row are
shown in the table below
Now, here is the basic one-way ANOVA table
Row Front Middle Back
Sample size 7 9 8
Mean 75.71 67.11 53.50
Variance 310.90 119.86 80.29
St. Dev 17.63 10.95 8.96
Source SS df MS F p
Between
Within
Total
13. Cont…
Grand Mean
In our example
The Between Group Variation for our example is
SS(B)=1902
1 1 2 2
1 2
k k
k
n x n x n x
x
n n n
SS(B)=7(75.71-65.08)2 + 9(67.11-65.08)2 + 8(53.5-65.08)2 =1902
7(75.71) + 9(67.11) +8(53.5)
x̅ = = 65.08
7 + 9 + 8
14. Cont…
The within group variation for our example is 3386
Degree of freedom
The between group df is one less than the number of groups
We have three groups, so df(B) = 2
The within group df is the sum of the individual df‘s of each group
The sample sizes are 7, 9, and 8
df(W) = 6 + 8 + 7 = 21
The total df is one less than the sample size
df(Total) = 24 – 1 = 23
SS(W) = 6(310.9)+8(119.86)+7(80.29) = 3386
15. Cont….
Variance (mean of squares)
MS(B)= 1902 / 2 = 951.0
MS(W)= 3386 / 21 = 161.2
MS(T)= 5288 / 23 = 229.9
Now computing ANOVA
Source SS df MS F p
Between 1902 2 951.0 5.9
Within 3386 21 161.2
Total 5288 23 229.9
16. Cont…
P(F2,21 > 5.9) = 0.009
There is enough evidence to support the claim that there is a
difference in the mean scores of the front, middle, and back
rows in class.
The ANOVA doesn‘t tell which row is different, we need to
look at confidence intervals or run post hoc tests to determine
that
18. What is meta analysis ?
―Meta-analysis is a statistical technique for combining the
results of independent, but similar, studies to obtain an
overall estimate of treatment effect.‖
Margaliot, Zvi, Kevin C. Chung. ―Systematic Reviews: A Primer for Plastic Surgery
Research.‖ PRS Journal. 120/7 (2007) p.1840
Quantitative approach for systematically combining results of
previous research to arrive at conclusions about the body of
research.
Each study produces a different estimate of the magnitude.
Meta-analysis combines the effects from all studies to give an
overall mean effect and other important statistics
19. The crack......
Quantitative : numbers
Systematic : methodical
Combining: putting together
Previous research: what's already done
Conclusions: new knowledge
20. Systematic reviews
―A review that is conducted according to clearly
stated, scientific research methods, and is
designed to minimize biases and errors inherent to
traditional, narrative reviews.‖
Systematic Reviews minimize bias.
A systematic review is a more scientific method of
because specific protocols are used to determine
which studies will be included in the review.‖
21. Systemic review vs. meta analysis
Systematic Reviews Meta-analyses
Identify and critique relevant
research studies
Discuss factors that may explain
heterogeneity
Synthesize the knowledge
Identify relevant research studies
using a defined protocol
Statistically test study
heterogeneity and investigate
explanatory variables.
Statistically summarize results to
obtain an overall estimate of
treatment effect.
22. Four Steps of Meta Analysis
Identifying studies
Determining eligibility of studies
o Inclusion: which ones to keep
o Exclusion: which ones to throw out
Abstract Data from the studies
Analyzing data in the studies statistically
23. Identifying studies
Being methodical: Defining the Research Question
Performing the literature search
List of popular databases to search
Pubmed/Medline
Embase
Cochrane Review/Trials Register
Other strategies ....
Hand search (in the library...)
Personal references, and emails
web, eg. Google search (http://scholar.google.com)
Selection of the studies
24. Eligibility of studies
Should be determined in advance, to reduce investigator bias
Cannot include all studies
Keep the ones with
high levels of evidence
good quality
check with QUOROM (Quality of reporting of systematic reviews)
guidelines
Usually, MA done with RCTs
Case series, and case reports definitely out
The QUOROM guidelines for reporting a meta-analysis requests that
investigators provide a flow diagram of the selection process.
26. Cont…
Selection problems are major problems
Criteria include but are not limited to:
Types of studies included (case control, cohort, etc)
Years of publication covered
Languages
Restrictions on sample size
Definition of disease, exposures
Confounders that must be measured
Dose response categories similar
27. The issue….
Unpublished studies that failed to yield significant results.
If substantial number of such studies , evaluation of the
overall significance level may be unduly optimistic.
A biased sample – a sample of only those publications
reporting statistically significant results
This bias inflates the probability of making a Type II error
FILE DRAWER PHENOMENA
28. Checking for publication bias
Funnel plots display the studies included in the meta
analysis in a plot of effect size against sample size
Smaller studies -more chance variability , the expected picture
is one of a symmetrical inverted funnel
Asymmetric plot suggests that the meta analysis may have
missed some trials – usually smaller studies showing no effect
Asymmetry could also occur if small studies tend to have
larger effect size
Interpretation…
30. Abstract the data
Data to be extracted from each study should be determined in
the design phase and……
A standardized form is to be constructed to record the data.
Examples of data commonly extracted
Study design, descriptions of study groups, diagnostic
information, treatments, length of follow-up evaluation, and
outcome measures.
31. What should be abstracted from
articles?
Should at least include:
Type of study
Source of cases/controls or cohort
Measures of association
Confidence intervals
Number of observations
Confounders adjusted for, if any
32. Plan of Action
ARE THE STUDIES ELIGIBLE FOR MA (STEP I)?
DISCARD
YES
NO
ENTER INTO A SPECIFIED FORMAT
ABSTRACT THE DATA
33. Analyzing data in the studies
statistically
Clinical trials present results as the frequency of some outcome in the
intervention groups and the control group.
Meta-analysis usually summarize as a ratio of the frequency of the
events in the intervention to that in the control group.
Most common summary measure of effect size are odds ratio
(OR),standard deviation (d) but RR and NNT are also seldom used
Separate methods used for combining effect size and other outcome
measures such as risk difference or hazard ratio
Categories: 0.2-small, 0.5-medium, 0.8-Large (Cohen, 1977).
34. Examples
Smith and Glass, 1977 synthesized the results from 400
controlled evaluations of psychotherapy and counselling to
determine whether psychotherapy ‗works‘.
They coded and systematically analyzed each study for the
kind of experimental and control treatments used and the
results obtained.
They were able to show that, on the average, the typical
psychotherapy client was better off than 75% of the untreated
‗control‘ individuals.
35. Examples….
Iaffaldano and Muchinsky (1985) found from their meta-
analysis that overall there is only a slight relationship
between workers‘ job satisfaction and the quality of their
performance.
Jenkins (1986) tracked down 28 published studies
measuring the impact of financial incentives on
workplace performance.
Only 57% of these found a positive effect on performance and the
overall effect was minimal.
The null hypothesis examined by the independetsamples ttest is that two population means are equal. (the experiment-wise α level) than the α level set for each t test.
The funnel plot has some limitations;for example, it can sometimes be difficult to detect asymmetry by eye.20 To helpwith this, formal statistical methods havebeen developed to test for heterogeneityEgger’s regression test