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Sudhakar singh meta analysis
1. META-ANALYSIS
1
SUDHAKAR SINGH
DEPARTMENT OF CLINICAL RESEARCH
36/MPH/DIPSAR/19
In Guidance of:-Prof. S.K GUPTA (PhD
Dsc FIPS FIACS)
Distinguised Professor DIPSAR, NEW
DELHI ,INDIA
2. OVERVIEW
2
1. History
2. Narrative Review
3. Introduction to Systematic Review &
Meta-analysis
4. Steps: 6S
5. Needs for Meta-analysis
6. Emerging methods in meta-analysis
7. Strengths of Meta-Analysis
8. Limitations of Meta-Analysis
9. Guidelines of Meta-Analysis
10. Meta-Analysis Software
3. HISTORY
Karl Pearson
Combined studies
to show the efficacy
of typhoid vaccine
Archibald Cochrane
Emphasized the need
to review all medical
topics
Gene Glass
Coined the term
‘meta-analysis’
‘Cochrane
Collaboration’
was established
1904 1972 1976 1993
3
4. NARRATIVE REVIEW:
4
● Gives a broad overview of relevant information tempered
by years of practical knowledge from an experienced
author
● Implicit process
● DRAWBACKS:
1. become less efficient with more data
2. cannot deal with variations in treatment effectiveness
3. not reproducible
5. INTRODUCTION
5
“A study of studies”
DEFINITIONS:
● SYSTEMATIC REVIEW: A systematic review is a
research summary that addresses a focused clinical
question in a structured, reproducible, unbiased manner
● META-ANALYSIS: It is a quantitative approach for
systematically combining results of previous research to
arrive at conclusions about the body of research.
6. What are Reviews?
Individual
studies
One review Why?
- Complex issue
- Small sample size in
individual studies
(increase precision)
- Resolve discrepancies
Narrative reviews
Systematic reviews (SR)
Meta-analyses (MA) of SR
7. How are SR and MA Conducted?
PICO
- Population
- In tervention
- Compar ator
- Outcome
1 Define the question
2 Search the literature
- MEDLINE,
EMBASE.. Etc.
- Grey literature
- Ask experts
- tfand search
3 Pull articles/Screen abstracts
- Apply inclusion/exclusion criteria
4 Read full paper
- Apply inclusion/exclusion criteria
5 Data abstraction
+ assess quality of included studies
6 Conduct analysis
i.e. perform Meta-
Analysis
8. HIERARCHY OF SCIENTIFIC EVIDENCE
8
Clinical
Practice
Guidelines
Meta Analysis and
Systemic Review
Randomized controlled trial
prospective , tests treatment
Cohort Studies
prospective-Exposed cohort is observed
for outcomes
Case Control studies
Retrospective – Subjects already of interest
looking for risk factors
Case Reports or Case Series
Narrative Reviews, Expert Opinions, Editorials
Animal and Laboratory Studies
Secondary, Pre
appraised, or
Filtered
Primary
studies
Observational
studies
10. 1. State: the research question and
the objectives
10
● RESEARCH QUESTION: analogous to the research
hypothesis in the primary research studies
●‘PICO’ Model for framing a researchquestion:
P: Population of interest
I: Intervention
C: Comparison (if relevant)
O: Outcome or endpoints
● Define the objective clearly: whether summarize or draw
conclusive evidence
11. 2. Search: Databases
11
● Provides the data for the review and analysis
● Important to create appropriate list of “KEYTERMS”
● Document key elements of each search: to ensure
reproducibility and prevent duplication
● Should include all studies pertaining to the keywords
unless otherwise specified (Exhaustive)
● Should be supplemented by searches of the trial registries,
bibliographies of retrieved articles, internet searches,
subject-specific and need-specific databases
● Incorporate ‘grey literature’ to diminish the risk of
publication bias and reporting bias
13. 3. Select: set eligibility criteria
13
● Similar to inclusion and exclusion criteria specified in a
clinical protocol
● Blind the reviewer (to the author, source and results of the
publication) to reduce selection bias
● Inclusion criteria should address the following
a) Type of study: RCTs/ Case Controls/ Observational
b) Patient characteristics
c) Treatment modalities: allowable treatment type, dosage,
duration
20. 5. Synthesize: Abstract data
20
● Specific guidelines should be mentioned in the protocol
● Done to capture relevant information in a concise
focused fashion
● Pilot testing the data abstraction form should be done on
a few studies before defining a final format
● The protocol should specify
a) The items
b) The information to be collected for each item
c) Format for collecting the items
22. 6. Summarize: Analyze results
and Document
22
● Evaluate the data for heterogeneity
● Pool the data
● Identify publication bias
23. 6. Summarize: Analyze results
and Document
23
A) HETEROGENEITY:
● Variability among studies
● Types: 1. Clinical Heterogeneity
2. Methodological Heterogeneity
3. Statistical heterogeneity
● If clinical and methodological heterogeneity is substantial,
it is appropriate to proceed with a qualitative systematic
review
● Sources: a) sampling error (within study variability)
b) true heterogeneity
24. Q TEST[1]
(chi squared test)
24
P value of ≤0.1 considered significant
If chi squared >degree of freedom, heterogeneity
present
I2 TEST[1] I2= 0%, variability due to sampling error within
studies
I2=25%, low heterogeneity
I2=50%, medium heterogeneity
I2=75% , high heterogeneity
τ2
TAU SQUARED[2] > 1, heterogeneity present
• Identifying and Measuring Heterogeneity
Either by visual inspection of the forest plot of studies or
by statistical tests
STATISTICALTEST SIGNIFICANCE
• Addressing heterogeneity :
a) Choosing not to pool data
b) Pool using random effects model/ Investigate using
subgroup analysis or meta-regression
25. ● SUBGROUPANALYSIS
➢ Subgroups are normally groups of studies organized
according to study-level variables
➢ Ideally, it should be planned a priori
➢ Consists of 2 parts:
1. Calculating summary outcome measure and statistical
heterogeneity in each subgroup
2. Statistical heterogeneity in each subgroup
● META-REGRESSION
➢ Graphical representation to simultaneously explore
multiple study level variables (co-variates) as potential
sources of heterogeneity
25
26. 6. Summarize: Analyze results and
Document
26
B) POOLING OF DATA
● Statistical Models for Pooling of Data
1. Fixed-Effects model (assumes that all studies in the
analysis are estimating the same common effect
size)
2. Random-Effects model (assumes that studies
included are sufficiently heterogeneous, each is
estimating the effect size in its respective population
or setting)
● Specific Statistical Pooling Methods
1. Mantel- Haenszel Method
2. Inverse Variance Method
3. Peto Method
27. • In a fixed effect
model , we assume
the studies come
from the same
hypothetical
population of
studies
• We assume a single,
‘fixed’, parameter
• Study weights are a
function of within
study variances
• Confidence interval
relatively narrow
FIXED EFFECTS MODEL
28. • There is no longer the
assumptions of a
single, homogeneous
source population.
• Studies are allowed to
come from different
distributions.
• Study weights not just
based on within-study
variances ,a random
effects constant is
used to distribute the
weights more evenly.
• Result in a wider
confidence interval.
RANDOM EFFECTS MODEL
29. 6. Summarize: Analyze results and
Document
29
B) POOLING OF DATA
● Representation: FOREST PLOT
➢ Graphical representation of a meta-analysis
➢ Usually accompanied by a table listing references
(author and date) of the studies included in the meta-
analysis
➢ The right hand column is a plot of the measure of
effect (e.g. Odds ratio) for each study (often
represented by a square) incorporating confidence
intervals represented by horizontal lines
➢ A black diamond at the bottom of the graph shows the
average effect size of the three studies
30. Forest plots. (A) (left) A homogeneous random-effects meta-analysis of trials to determine
the effect of streptokinase on overall mortality in patients with acute myocardial infarction.
(B) (right) A moderately heterogeneous random-effects meta-analysis of trials to determine the
benefits of acetylcysteine in reducing contrast-induced nephropathy in patients undergoing
angiography. RR: risk ratio; CI: confidence interval; n: number or events; N: total number of patients.
30
31. 6. Summarize: Analyze results and
Document
31
C) PUBLICATION BIAS
● Publication bias refers to the tendency of studies
with statistically significant results to be published
compared with studies with non-significant results of
meta-analysis
● PREVENTION:
a) Include all relevant studies (positive or negative)
b) Compulsory and prospective registration and
reporting of results in ClinicalTrials.gov/ctri.nic.in
32. C) PUBLICATION BIAS
32
DETECTION:
Funnel Plot: Exploratory tool used to visually assess the
possibility of publication bias in a meta-analysis. It is a
scatter plot of magnitude of association (x-axis) against
the estimated precision (y-axis)
[Note: A symmetric funnel plot is suggestive but does not
prove the absence of publication bias, nor does an
asymmetric plot prove publication bias]
Statistical approaches to detect publication bias:
(a) Begg’s test
(b) Egger’s test
34. NEED FOR META-ANALYSIS
34
● Explosion of data in the last decade
◦ 2.5 million new scientific papers are published each year
◦ Increasing at a rate of 4-5% per year
◦ >10,000 meta-analyses and qualitative systematic reviews are
published annually
● Multiple studies done on the same focused study topic
generate overwhelming data, hence the need to
summarize in a method which is scientifically
acceptable
● To interpret contradictory data in different studies
35.
36. EMERGING METHODS FOR
META-ANALYSIS
36
1. NETWORK META-ANALYSIS
◦ Compares several, rather than just 2, treatments
simultaneously in a single statistical model
2. BAYESIAN METHODS
◦ Bayesian statistical methods provide a way to
incorporate prior knowledge about a risk factor into
a meta-analysis
3. INDIVIDUAL PATIENT LEVELSTUDIES
◦ Investigators collaborate to combine individual
patient-level data and performed pooled analysis
37. Strengths of Meta - Analysis
• A major advantage of a meta-analysis is that it produces a
precise estimate of the effect size with considerably
increased statistical power, which is especially important
when the power of the primary study is limited because of
the small sample size
• It is also an objective, quantitative method that provides a
less biased estimate on a specific topic.
• It enables comparison or combination of results across sets
of similar studies this giving more dependable results since
this may help to remove any inconsistencies.
• It is also more useful to convey a combined summary than
to depict the results for each of the individual studies.
38. Strengths of Meta-Analysis
Continued
• Precision and Accuracy of the Results is overall increased.
• A statistical tool involved in meta analysis identifies the
existence of evidence gap and methodological problems
within the body of literature .
• Easily convey the required information to healthcare
providers, reseachers, and policy makers
39. LIMITATIONS OF META ANALYSIS
•If studies are too heterogeneous to be comparable, a meta-
analysis should be avoided, as the meta-analysis result may
be meaningless and any true effect may be obscured.
•Publication bias, is one of the main shortcoming of Meta
Analysis(there is always a tendency that studies with
positive result are more likely to published than the studies
with negative result.
•The other limitation of meta-analysis is “garbage in,
garbage out,” which means that if a meta-analysis includes
low-quality studies with bias, the results of the meta-analysis
will be biased and incorrect.
•Meta-analysis cannot improve the quality or reporting of
the original studies. It does not offer nothing much new.
40. Limitation of Meta-Analysis Continue
• A meta-analysis of several small studies does not predict
the results of a single large study.
• Inability to deal with the large number of studies on a
topic hence focus may be limited on a small subset of
studies in many occasions without describing how the
subset was selected.
• Much care should be accorded in reviewing and
comparing results and even citation of previous reviews.
41. Guidelines for Meta-Analysis
The QUORUM Statement :
•Quality of Reporting of meta analysis –For clinical
Randomized Controlled Trials (RCT’s)
MOOSE Quidelines:
•Meta Analysis of Observational Studies in
Epidemiology
42.
43. META-ANALYSIS SOFTWARE
o Free
•RevMan[Review Manager]
•Meta- Analyst
•Epi Meta
•Easy MA
•Meta-Test
•Meta-Stat
oCommercial
•Comprehensive Meta-
analysis
•Meta-Win
•Weasy MA
oGeneral stats packages
•Stata
•SAS
•S-Plus
44. REFERENCES
1. Gupta SK, Srivastava Sushma, Drug Discovery & Clinical Research, 2nd
Edition, Jaypee Brothers Medical Publishers (P) Ltd. Daryaganj New Delhi
2019, 274-288
2. Geoff D. Finding the Truth – research methods in the biomedical sciences. VIII
– RCTs and meta-analysis – the gold and platinum standards of evidence in
biomedical research? [Internet]. Dr Geoff. 2020
3. Fagard R, Staessen J, Thijs L. Advantages and disadvantages of the meta-
analysis approach. Journal of Hypertension. 1996;14(Supplement 2):S9-S13.
4. Ioannidis J, Lau J. Pooling Research Results: Benefits and Limitations of Meta-
Analysis. The Joint Commission Journal on Quality Improvement.
1999;25(9):462-469.
5. Nandhini J, Ramasamy S, Ramya K, Kaul RN, Felix A J, Austin RD. Is
nonsurgical management effective in temporomandibular joint disorders? – A
systematic review and meta-analysis. Dent Res J 2018;15:231-41
