Systematic reviews and meta-analyses aim to summarize research evidence on a topic. This document provides an overview of how to conduct systematic reviews and meta-analyses, including formulating a question, identifying relevant studies, extracting data, assessing bias, synthesizing data through meta-analysis if appropriate, interpreting results, and updating reviews. Key steps involve developing eligibility criteria, searching multiple databases, assessing risk of bias, addressing heterogeneity, and evaluating for publication bias. Conducting reviews using standardized methods helps provide reliable conclusions to inform clinical practice and policy-making.

1. OVERVIEW OF SYSTEMATIC REVIEW
AND META- ANALYSIS
DR SNEHA
POST GRADUATE
DEPARTMENT OF COMMUNITY MEDICINE

2. SYSTEMATIC REVIEW
“ Systematic review is a high level overview of a
particular research question that systematically
identifies, selects, evaluates and synthesizes all high
quality research evidence relevant to that question in
order to answer it”

3. “ A systematic review is a summary of the medical
literature that uses explicit and reproducible methods
to systematically search, critically appraise and
synthesize on a specific issue”
• It synthesizes the results of multiple primary studies
related to each other by using strategies that reduce
biases and random errors.

4. • Often written by panel of experts after reviewing all
the information from both published and unpublished
literature ( grey literature).
• Useful for policy makers for decision making on any
intervention/ treatments, etc.

5. • Forms the basis of decision making in evidence based
medicine and evidence based behavioral practice.
• Hierarchy of evidence: top position
• Most reliable evidence with less bias.
• Considered gold standard.

6. PYRAMID OF
EVIDENCE IN
RESEARCH

7. CHALLENGES FACED DURING SYSTEMATIC
REVIEW
• Too many studies to search
• Publication bias: only studies with significant findings
are published.
• Inconsistencies in findings because of flaws in study
conduct or random errors.

8. STEPS IN CONDUCTING SYSTEMATIC REVIEW
1. Formulate review question
2. Identification of relevant studies
3. Extraction of data
4. Assessment of bias in included studies
5. Synthesis of data
6. Interpreting the evidence
7. Writing up the review
8. Updating the review.

9. 1. FORMULATE REVIEW QUESTION
• Address the variety of issues: incidence/ prevalence/
etiology of diseases / diagnostic accuracy/
effectiveness of interventions.
• Should be precise.
• Should specify key components: PICO

10. P= Participants
I = Interventions
C = Control / comparison
O = Outcome of interest.

11. Eg:
“ in patients with TB does daily regimen or alternative regimen have
effective success rate “
PARTCIPANT INTERVENTION CONTROL
OUTCOME OF
INTEREST

12. 2. IDENTIFY RELEVANT STUDIES
• Time consuming
• Eligibility criteria should be set for inclusion and
exclusion.
• Criteria is set up with relevance to PICO components

13. CRITERIA WITH REGARD TO EACH COMPONENTS
P= PARTCIPANTS
I=INTERVENTIONS
C=CONTROL
O = OUTCOME
Sociodemographic characteristics and study
setting
What intervention? how delivered? Who delivers?
Intensity of intervention?
What kind of comparison?
Active control : different regimen of same drugs/ diff therapy
Inactive control: placebo/ standard protocol/ no Rx
Should be clearly stated.
Type of outcomes: short term or long term
Primary or secondary outcome
One benefit and one ADR should also be assessed.

14. • Define the study design also based on the research
question.
1. Prevalence of diseases/ diagnostic accuracy of tests:
cross sectional design
2. Aetiology of disease : cohort design
3. Effects of intervention: RCT

15. SEARCHING FOR STUDIES
SOURCES: MEDLINE, EMBASE, CENTRAL, LILAC, etc
• Can use MeSH terms to search articles.
• Search for unpublished literature and ongoing studies
• Unpublished literature/ grey literature: conference
abstracts, dissertations, books, etc.
• Studies in non English journals and small sample size
studies to be selected too.

16. PRISMA GUIDELINES FOR STUDY SELECTION

17. 3. DATA COLLECTION AND EXTRACTION
WHAT DATA TO BE COLLECTED?
Data regarding;
• Eligibility of study
• Study methodology
• Details of participants
• No of intervention groups and details specific to
interventions given

18. • Information regarding outcomes: definitions, how
measured, all pre specified and unspecified outcomes
are to be collected and analysed.
• Information on ethical approval, funding, conflicts of
interest, name and contact of authors.

19. DATA EXTRACTION
• Process of data recording into data collection forms.
• Two reviewers should work independently: to reduce
the risk of errors.
• Blinding of data extractors: to reduce the risk of bias.
• However routine blinding is not usually
recommended.
• Check for study duplication: same studies reported in
more than one journal.

20. 4. ASSESSING THE PRESENCE AND RISK OF
BIAS
• Assess for bias in study design, conduct, analysis or
reporting of study.
• Several methods available.
• For clinical trials: DOMAIN BASED EVALUATION
recommended by Cochrane library.

21. TYPE OF BIAS DESCRIPTION DOMAIN
SELECTION BIAS Differences in the baseline
characteristics of the
participants in the groups
compared
Allocation concealment
PERFORMANCE
BIAS
Differences in the care
given to groups
Blinding
ATTRITION BIAS Differences in the
withdrawals
Blinding
DETECTION BIAS Differences in how
outcome is measured
Blinding
REPORTING BIAS Publication bias Selective outcome
reporting

22. 5. SYNTHESIS OF DATA: Meta analysis
6. INTERPRETING THE EVIDENCE
7. WRITING THE REVIEW: PRISMA guidelines
8. UPDATING THE REVIEW

23. ADVANTAGES OF SYSTEMATIC REVIEW
• Uses explicit methods which limits bias
• Draws reliable and accurate conclusions
• Best form of evidence
• Very useful decision making tools for clinicians,
researchers and for policy makers.
• Generation of new hypothesis about subgroups of
study
• Increases the precision of the results.

24. LIMITATIONS OF SYSTEMATIC REVIEW
• Location and selection of studies
• Heterogeneity
• Loss of information on important outcomes
• Inappropriate subgroup analyses

25. META-ANALYSIS (MA)
ACCORDING TO GENE GLASS WHO FIRST DEFINED
META ANALYSIS IN 1976,
“ meta analysis refers to a statistical analysis of a large
collection of analysis results from individual studies ,
for the purpose of integrating the findings.”

26. TYPES OF META ANALYSIS
1. CUMULATIVE MA: new studies are added and MA
repeated every time an new study is published.
2. RETROSPECTIVE MA: commonly done.
3. PROSPECTIVE MA: criteria and protocol for selection
is stated even before studies of interest are
published. ( low bias)

27. EFFECT SIZE IN META ANALYSIS
EFFECT SIZE:
• measure of analysis
• Dependent variable
• Any standard index
• Eg: prevalence, incidence, odds ratio, relative risk,
effects of intervention.

28. TYPE OF ANALYSIS DONE IN MA
ANALYSIS
SECONDARY
ANALYSIS
SUBGROUP
ANALYSIS
SENSITIVITY
ANALYSIS
PRIMARY ANALYSIS
MAIN OBJECTIVE
AND POOLED
ESTIMATE OF
EFFECT SIZE .

29. SUBGROUP ANALYSIS
• If a meta analysis is performed across heterogenous
trials, it may be inappropriate to draw conclusions
from the pooled treatment effect .
• If the same trials are subgrouped and there is no
heterogeneity within trials then valid conclusions can
be drawn using results from subgroup analysis.

30. • If subgroup analysis demonstrate that the treatment is
more or less effective for certain subgroups of
patients, interpretation of these subgroup analyses
can provide valuable insight into how the treatment
should be used in clinical practice.
• Participants are divided into subgroups based on
certain characteristics ( gender, ses) or trial
characteristics ( geographic location) and then
analysed

31. SENSITIVITY ANALYSIS
• done to see if the estimate changes by changing some
parameters.
• To see how far the result is affected by changes.
• Eg: estimates are checked before and after including
low quality studies

32. PRESENTATION OF THE RESULT OF MA:
FOREST PLOT
• Graphical representation of the results
• Always included in presenting the results.
• Displays the effect size estimates and confidence
intervals for each study included in MA.

33. • Studies to be ordered either according to ;
 effect size estimate/ magnitude
Study weightage ( precision)
Chronological order
Any other meaningful order

35. HOW TO INTERPRET THE RESULT OF META ANALYIS??

37. WERE APPLES COMBINED WITH ORANGES??

38. LOOK FOR HETEROGENIETY
•Refers to difference between studies not due to
chance .
•Types of heterogeneity : clinical ( pt characteristics,
interventions, outcomes) and statistical ( diff in study
design and quality).
•Clinical heterogeneity always exists and can be
identified without any calculation or tests.
•Statistical heterogeneity doesn’t exist always and
needs tests to identify them.

39. •HOW TO DETECT HETEROGENIETY?
1. REVIEW TABLES AND CHECK FOR THE TYPE OF PTS:
for clinical heterogeneity . ( mixing of pts with
different diseases and treatment pattern)
2. EYEBALL TEST: look at the forest plot for overlapping
of confidence interval. (Overlap + = no
heterogeneity).
3. STATISTICAL TEST: tells the extent of heterogeneity
and its significance.

40. OVERLAPING OF
CONFIDENCE INTERVAL+.
NO HETEROGENIETY
NO OVERLAPPING OF
CONFIDENCE INTERVAL.
HETEROGENIETY +

41. STATISTICAL TEST OF HETEROGENIETY
1. x2 test : commonly used
• If P >0.05 heterogeneity +
• Not useful
2. I2 test: to quantify heterogeneity
• I2 = % of variation across studies that is due to heterogeneity and
not due to chance.
• 25%= low heterogeneity ( 25% of variation is not due to chance)
• 50%= moderate
• 75% = high

42. NO HETEROGENIETY
HETEROGENIETY +

43. WHY IS IT IMPORTANT TO KNOW ABOUT
HETEROGENIETY?
“ LARGE HETEROGENIETY AMONG STUDIES MAY MAKE ANY POOLED
ESTIMATE MEANINGLESS”

44. FIXED AND RANDOM EFFECT MODEL
• FIXED EFFECT: differences among studies are purely due to
chance .
• RANDOM EFFECT: differences among studies due to chance
and other reasons also.
• WHICH MODEL TO BE USED?
 when heterogeneity is absent: use fixed effect
When heterogeneity is present: use random model
Some researchers suggest to use both the models
irrespective of heterogeneity.

45. NO HETEROGENIETY
HETEROGENIETY +
USED FIXED MODEL
USED RANDOM
EFFECT MODEL

46. WAYS TO DEAL WITH HETEROGENIETY
• Do not perform MA
• Do subgroup and sensitivity analysis: to find the
reason for heterogeneity.
• Do MA based on random model: use only when the
reason for heterogeneity cannot be explained.
• Change the effect measure: may sometimes introduce
artificial heterogeneity

47. Publication bias analysis : funnel plot
• Happens when studies with positive and significant results are only
selected.
• Tool to visually assess the possibility of publication or small study bias
in meta analysis.
• Scatter plot of effect size over standard error of effect size.
• X axis: effect size
• Y axis: SE of effect size.
• Not recommended in a very small study MA ( n<10)
• Studies with smaller sample size are scattered at the bottom of the
plot.
• Large and most powerful studies at the top.

49. Asymmetry in a funnel plot
• Publication bias
• Poor methodology
• By chance or random error
• True heterogeneity
To differentiate between publication
bias and other reasons of plot
asymmetry
CONTOUR ENHANCED
FUNNEL PLOT

51. No studies at the bottom
of the plot. No smaller
studies included .
Possibility of publication
bias .

52. CONTOUR ENHANCED FUNNEL PLOT:
• Funnel plot with additional contour lines of statistical
significance
• Lines at p=0.01,0.00,0.05,etc..
Interpretation:
 if studies missing in area of non significance: PUBLICATION
BIAS
If studies missing in areas of significance: OTHER REASONS
No studies in areas of significance : PUBLICATION BIAS

53. Studies missing in the area
of non significance.
PUBLICATION BIAS .

54. studies missing in the
area of significance .
OTHER REASON FOR
BIAS

55. QUALITY ASSESSMENT OF SYSTEMATIC
REVIEW AND META ANALYSIS
3 commonest ways ;
1. Overview quality assessment questionnaire
2. PRISMA checklist
3. The AMSTAR tool

56. CHECKLIST FOR STUDIES
S.NO STUDY DESIGN GUIDELINES
1. CASE REPORT CARE
2. OBSERVATIONAL STUDY STROBE
3. ANALYTICAL STUDY STROBE
4. RCT CONSORT
5. NON RANDOMIZED CONTROLLED TRIAL TREND
6. SYSTEMATIC REVIEW AND META ANALYSIS PRISMA
7. DIAGNOSTIC TEST ACCURACY QUADAS, STARD
8. INTERVENTIONS FOR QUALITY AND SAFETY OF CARE SQURE
9. ECONOMIC EVALUATION OF HEALTH INTERVENTIONS CHEERS
10. QUALITATIVE STUDY COREQ