SlideShare a Scribd company logo
1 of 42
Analysis and Interpretation: Overview ,[object Object],[object Object],[object Object],[object Object],[object Object]
Framework for synthesis ,[object Object],[object Object],[object Object],[object Object],[object Object]
Why Perform a Meta-analysis? ,[object Object],[object Object],[object Object],[object Object],[object Object]
More on Meta-analysis ,[object Object],[object Object],[object Object],[object Object],[object Object]
When not Appropriate to do M/a ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Dichotomous Measures ,[object Object],[object Object],[object Object],[object Object]
Risk ratio (RR)  aka relative risk RR =   a / (a+b)  c / (c+d) Risk/ probability/ chance  of the occurrence of an event in treatment relative to control Intervention Control a+b=n I c+d=n C Event No event d c b a
Sample RR Calculation Death No death RR =   14/133  =  0.11  = 0.13 128/148  0.86 Drug 133 148 Placebo 20 128 119 14
Odds ratio (OR) Intervention Control No event Event OR =  a / b  c / d  Odds of an event occurring to it not occurring for treatment relative to control a+b=n I c+d=n C d c b a
Sample OR Calculation Death No death Drug Placebo 133 148 OR =   14/119  =  0.12  = 0.019 128/20  6.4 20 128 119 14
Interpreting (for intervention) Increased odds (harmful) Increased odds  (beneficial) OR>1 (6.4/0.12) Reduced odds (beneficial) Reduced odds (not beneficial) OR<1 (0.12/6.4) No difference No difference OR=1, RR=1 Increased risk (harmful) Increased risk  (beneficial) RR>1 (0.86/0.11) Reduced risk  (beneficial) Reduced risk (not beneficial) RR<1 (0.11/0.86) Bad outcome  (e.g. infection) Good outcome (e.g. remission)
RR vs. OR ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Closer Look at Odds RR = 0.11 / 0.86 = 0.13 ↑ A rate (11%) OR = 0.12 / 6.4  = 0.019 ↑ ~1:9 ↑ ~7:1
Absolute Effect Measures ,[object Object],[object Object]
Risk Difference (RD) Death No death Actual difference in  risk of events Placebo Drug 133 148 RD = 14/133 – 128/148 = 0.11 – 0.86 = - 0.75 20 128 119 14
Risk Difference (RD)  (continued) ,[object Object],[object Object],[object Object]
NNT ,[object Object],[object Object],[object Object],[object Object],[object Object]
Uncertainty ,[object Object],[object Object],[object Object],[object Object]
Which effect measure for meta-analysis? ,[object Object],[object Object],[object Object]
Meta-analysis in RevMan
Meta-analysis in RevMan  (continued) ,[object Object],[object Object]
Fixed vs Random Effects ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Fixed Effects Analysis in Picture View
Random Effects Analysis in Picture View
Random effects in RevMan 5 ←  DerSimonian and Laird  random effects model
Random effects in RevMan 5  (continued) ←  DerSimonian and Laird  random effects model
Sample Forest plot (RR) ,[object Object]
Meta-analysis for Continuous Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mean Difference (MD) ,[object Object],[object Object]
Standardized Mean Difference (SMD) ,[object Object]
Heterogeneity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
    Clinical and Methodologic Heterogeneity  ,[object Object],[object Object],[object Object]
Statistical Heterogeneity ,[object Object],[object Object]
Q test ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
I 2  Statistic ,[object Object],[object Object],[object Object]
I 2  Statistic  (continued) * Importance of I 2  value depends on: ●  magnitude and direction of effects ●  strength of evidence of heterogeneity - Chi-squared P value, or - I 2  confidence interval Considerable heterogeneity*  75% to 100% May represent substantial heterogeneity* 50% to 90% May represent moderate heterogeneity* 30% to 60% Might not be important 0% to 40% Guide to Interpretation I 2  value
Sample Forest Plot:  Q and I 2
What to do with (Statistical) Heterogeneity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What to do with (Statistical) Heterogeneity ,[object Object],[object Object],[object Object],[object Object]
Subgroup and Meta-regression ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Subgroup Analysis
Sensitivity Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object]

More Related Content

What's hot

Confidence intervals
Confidence intervalsConfidence intervals
Confidence intervals
Tanay Tandon
 

What's hot (20)

Confidence intervals
Confidence intervalsConfidence intervals
Confidence intervals
 
6. Categorical data analysis - Chi-Square & Fisher Exact Test
6. Categorical data analysis - Chi-Square & Fisher Exact Test6. Categorical data analysis - Chi-Square & Fisher Exact Test
6. Categorical data analysis - Chi-Square & Fisher Exact Test
 
Overview of different statistical tests used in epidemiological
Overview of different  statistical tests used in epidemiologicalOverview of different  statistical tests used in epidemiological
Overview of different statistical tests used in epidemiological
 
Multiple Regression and Logistic Regression
Multiple Regression and Logistic RegressionMultiple Regression and Logistic Regression
Multiple Regression and Logistic Regression
 
Hazard ratios
Hazard ratiosHazard ratios
Hazard ratios
 
General Introduction to Health research (Basic)
General Introduction to Health research (Basic)General Introduction to Health research (Basic)
General Introduction to Health research (Basic)
 
Sample size calculation for cohort studies
Sample size calculation for cohort studies Sample size calculation for cohort studies
Sample size calculation for cohort studies
 
Sample size calculations
Sample size calculationsSample size calculations
Sample size calculations
 
Bias in clinical research
Bias in clinical research Bias in clinical research
Bias in clinical research
 
Causal Inference PowerPoint
Causal Inference PowerPointCausal Inference PowerPoint
Causal Inference PowerPoint
 
Types of bias
Types of biasTypes of bias
Types of bias
 
Cross sectional study
Cross sectional studyCross sectional study
Cross sectional study
 
Metaanalysis copy
Metaanalysis    copyMetaanalysis    copy
Metaanalysis copy
 
Meta analysis
Meta analysisMeta analysis
Meta analysis
 
Sample Size Determination
Sample Size DeterminationSample Size Determination
Sample Size Determination
 
Sample size calculation
Sample  size calculationSample  size calculation
Sample size calculation
 
Intent-to-Treat (ITT) Analysis in Randomized Clinical Trials
Intent-to-Treat (ITT) Analysis in Randomized Clinical TrialsIntent-to-Treat (ITT) Analysis in Randomized Clinical Trials
Intent-to-Treat (ITT) Analysis in Randomized Clinical Trials
 
Bias and confounder
Bias and confounderBias and confounder
Bias and confounder
 
Meta analysis
Meta analysisMeta analysis
Meta analysis
 
Odds ratio
Odds ratioOdds ratio
Odds ratio
 

Similar to Analysis and Interpretation

Baker esni handouts slides
Baker esni handouts slidesBaker esni handouts slides
Baker esni handouts slides
BartsMSBlog
 
Introduction_klsfnsfsnfsgnkgni _to_meta-analysis.ppt
Introduction_klsfnsfsnfsgnkgni _to_meta-analysis.pptIntroduction_klsfnsfsnfsgnkgni _to_meta-analysis.ppt
Introduction_klsfnsfsnfsgnkgni _to_meta-analysis.ppt
AnnaMarieAndalRanill
 
2016 Symposium Poster - statistics - Final
2016 Symposium Poster - statistics - Final2016 Symposium Poster - statistics - Final
2016 Symposium Poster - statistics - Final
Brian Lin
 
Importance of evidence
Importance of evidenceImportance of evidence
Importance of evidence
sahughes
 
Research methods 2 operationalization &amp; measurement
Research methods 2   operationalization &amp; measurementResearch methods 2   operationalization &amp; measurement
Research methods 2 operationalization &amp; measurement
attique1960
 
Quantitative_analysis.ppt
Quantitative_analysis.pptQuantitative_analysis.ppt
Quantitative_analysis.ppt
mousaderhem1
 
25_Anderson_Biostatistics_and_Epidemiology.ppt
25_Anderson_Biostatistics_and_Epidemiology.ppt25_Anderson_Biostatistics_and_Epidemiology.ppt
25_Anderson_Biostatistics_and_Epidemiology.ppt
PriyankaSharma89719
 

Similar to Analysis and Interpretation (20)

Critical Appriaisal Skills Basic 1 | May 4th 2011
Critical Appriaisal Skills Basic 1 | May 4th 2011Critical Appriaisal Skills Basic 1 | May 4th 2011
Critical Appriaisal Skills Basic 1 | May 4th 2011
 
Biostatistics clinical research & trials
Biostatistics clinical research & trialsBiostatistics clinical research & trials
Biostatistics clinical research & trials
 
Baker esni handouts slides
Baker esni handouts slidesBaker esni handouts slides
Baker esni handouts slides
 
Baker esni handouts reading papers
Baker esni handouts reading papersBaker esni handouts reading papers
Baker esni handouts reading papers
 
bio statistics for clinical research
bio statistics for clinical researchbio statistics for clinical research
bio statistics for clinical research
 
Introduction_klsfnsfsnfsgnkgni _to_meta-analysis.ppt
Introduction_klsfnsfsnfsgnkgni _to_meta-analysis.pptIntroduction_klsfnsfsnfsgnkgni _to_meta-analysis.ppt
Introduction_klsfnsfsnfsgnkgni _to_meta-analysis.ppt
 
Sample Size Estimation and Statistical Test Selection
Sample Size Estimation  and Statistical Test SelectionSample Size Estimation  and Statistical Test Selection
Sample Size Estimation and Statistical Test Selection
 
Meta analysis with R
Meta analysis with RMeta analysis with R
Meta analysis with R
 
2016 Symposium Poster - statistics - Final
2016 Symposium Poster - statistics - Final2016 Symposium Poster - statistics - Final
2016 Symposium Poster - statistics - Final
 
Analytic Methods and Issues in CER from Observational Data
Analytic Methods and Issues in CER from Observational DataAnalytic Methods and Issues in CER from Observational Data
Analytic Methods and Issues in CER from Observational Data
 
Bgy5901
Bgy5901Bgy5901
Bgy5901
 
Quantitative critical appraisal october 2015
Quantitative critical appraisal october 2015Quantitative critical appraisal october 2015
Quantitative critical appraisal october 2015
 
Displaying your results
Displaying your resultsDisplaying your results
Displaying your results
 
Bias and validity
Bias and validityBias and validity
Bias and validity
 
Importance of evidence
Importance of evidenceImportance of evidence
Importance of evidence
 
Research methods 2 operationalization &amp; measurement
Research methods 2   operationalization &amp; measurementResearch methods 2   operationalization &amp; measurement
Research methods 2 operationalization &amp; measurement
 
Quantitative_analysis.ppt
Quantitative_analysis.pptQuantitative_analysis.ppt
Quantitative_analysis.ppt
 
Session1b.ppt
Session1b.pptSession1b.ppt
Session1b.ppt
 
25_Anderson_Biostatistics_and_Epidemiology.ppt
25_Anderson_Biostatistics_and_Epidemiology.ppt25_Anderson_Biostatistics_and_Epidemiology.ppt
25_Anderson_Biostatistics_and_Epidemiology.ppt
 
Overview Of Ich New E9
Overview Of Ich New E9Overview Of Ich New E9
Overview Of Ich New E9
 

More from Francisco J Grajales III

Perceptions and attitudes toward virtual-reality medical training: The Ann My...
Perceptions and attitudes toward virtual-reality medical training: The Ann My...Perceptions and attitudes toward virtual-reality medical training: The Ann My...
Perceptions and attitudes toward virtual-reality medical training: The Ann My...
Francisco J Grajales III
 

More from Francisco J Grajales III (20)

Patient perspectives on (an)onymous data sharing
Patient perspectives on (an)onymous data sharingPatient perspectives on (an)onymous data sharing
Patient perspectives on (an)onymous data sharing
 
Using Social Media in (Evidence-Based Emergency) Medicine: A Primer for Pract...
Using Social Media in (Evidence-Based Emergency) Medicine: A Primer for Pract...Using Social Media in (Evidence-Based Emergency) Medicine: A Primer for Pract...
Using Social Media in (Evidence-Based Emergency) Medicine: A Primer for Pract...
 
Multi-User Virtual Environments in Healthcare: A Primer for Healthcare Students
Multi-User Virtual Environments in Healthcare: A Primer for Healthcare StudentsMulti-User Virtual Environments in Healthcare: A Primer for Healthcare Students
Multi-User Virtual Environments in Healthcare: A Primer for Healthcare Students
 
Pianissisimo: A Primer on Communicating with Decision Makers
Pianissisimo: A Primer on Communicating with Decision Makers Pianissisimo: A Primer on Communicating with Decision Makers
Pianissisimo: A Primer on Communicating with Decision Makers
 
Las TIC y la Sanidad 2.0: Al infinito y mas allá
Las TIC y la Sanidad 2.0: Al infinito y mas alláLas TIC y la Sanidad 2.0: Al infinito y mas allá
Las TIC y la Sanidad 2.0: Al infinito y mas allá
 
Panorama Internacional del eHealth
Panorama Internacional del eHealthPanorama Internacional del eHealth
Panorama Internacional del eHealth
 
To tweet or not to tweet? : Exploring the use of Social Media for [public] he...
To tweet or not to tweet? : Exploring the use of Social Media for [public] he...To tweet or not to tweet? : Exploring the use of Social Media for [public] he...
To tweet or not to tweet? : Exploring the use of Social Media for [public] he...
 
Perceptions and attitudes toward virtual-reality medical training: The Ann My...
Perceptions and attitudes toward virtual-reality medical training: The Ann My...Perceptions and attitudes toward virtual-reality medical training: The Ann My...
Perceptions and attitudes toward virtual-reality medical training: The Ann My...
 
Forumclinic Reshape 09
Forumclinic Reshape 09Forumclinic Reshape 09
Forumclinic Reshape 09
 
E-Government and E-Health Strategies by Mrs. Veronica Boateng
E-Government and E-Health Strategies by Mrs. Veronica BoatengE-Government and E-Health Strategies by Mrs. Veronica Boateng
E-Government and E-Health Strategies by Mrs. Veronica Boateng
 
eHealth : the promise of ICT for improving health in Africa
eHealth : the promise of ICT for improving health in AfricaeHealth : the promise of ICT for improving health in Africa
eHealth : the promise of ICT for improving health in Africa
 
ICT Infrastructure in Africa and the uptake of e-Health
ICT Infrastructure in Africa and the uptake of e-HealthICT Infrastructure in Africa and the uptake of e-Health
ICT Infrastructure in Africa and the uptake of e-Health
 
Electronic Health Records Global Perspectives
Electronic Health Records Global PerspectivesElectronic Health Records Global Perspectives
Electronic Health Records Global Perspectives
 
Interoperability Standards: The Key to Mobile Data
Interoperability Standards:The Key to Mobile DataInteroperability Standards:The Key to Mobile Data
Interoperability Standards: The Key to Mobile Data
 
Public Health Informatics in Africa Examples from IDRC supported projects in ...
Public Health Informatics in Africa Examples from IDRC supported projects in ...Public Health Informatics in Africa Examples from IDRC supported projects in ...
Public Health Informatics in Africa Examples from IDRC supported projects in ...
 
Role of standards, consumer education and media literacy
Role of standards, consumer education and media literacyRole of standards, consumer education and media literacy
Role of standards, consumer education and media literacy
 
Quality and safety of health information on the Internet: Who decides and ho...
Quality and safety of health information on the Internet: Who decides and ho...Quality and safety of health information on the Internet: Who decides and ho...
Quality and safety of health information on the Internet: Who decides and ho...
 
Quality and safety of health information on the Internet: Who decides and ho...
Quality and safety of health information on the Internet: Who decides and ho...Quality and safety of health information on the Internet: Who decides and ho...
Quality and safety of health information on the Internet: Who decides and ho...
 
Quality and safety of health information on the Internet: Who decides and ho...
Quality and safety of health information on the Internet: Who decides and ho...Quality and safety of health information on the Internet: Who decides and ho...
Quality and safety of health information on the Internet: Who decides and ho...
 
Quality and safety of health information on the Internet: Who decides and ho...
Quality and safety of health information on the Internet: Who decides and ho...Quality and safety of health information on the Internet: Who decides and ho...
Quality and safety of health information on the Internet: Who decides and ho...
 

Recently uploaded

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 

Recently uploaded (20)

How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Introduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDMIntroduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDM
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
AI in Action: Real World Use Cases by Anitaraj
AI in Action: Real World Use Cases by AnitarajAI in Action: Real World Use Cases by Anitaraj
AI in Action: Real World Use Cases by Anitaraj
 

Analysis and Interpretation

  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7. Risk ratio (RR) aka relative risk RR = a / (a+b) c / (c+d) Risk/ probability/ chance of the occurrence of an event in treatment relative to control Intervention Control a+b=n I c+d=n C Event No event d c b a
  • 8. Sample RR Calculation Death No death RR = 14/133 = 0.11 = 0.13 128/148 0.86 Drug 133 148 Placebo 20 128 119 14
  • 9. Odds ratio (OR) Intervention Control No event Event OR = a / b c / d Odds of an event occurring to it not occurring for treatment relative to control a+b=n I c+d=n C d c b a
  • 10. Sample OR Calculation Death No death Drug Placebo 133 148 OR = 14/119 = 0.12 = 0.019 128/20 6.4 20 128 119 14
  • 11. Interpreting (for intervention) Increased odds (harmful) Increased odds (beneficial) OR>1 (6.4/0.12) Reduced odds (beneficial) Reduced odds (not beneficial) OR<1 (0.12/6.4) No difference No difference OR=1, RR=1 Increased risk (harmful) Increased risk (beneficial) RR>1 (0.86/0.11) Reduced risk (beneficial) Reduced risk (not beneficial) RR<1 (0.11/0.86) Bad outcome (e.g. infection) Good outcome (e.g. remission)
  • 12.
  • 13. Closer Look at Odds RR = 0.11 / 0.86 = 0.13 ↑ A rate (11%) OR = 0.12 / 6.4 = 0.019 ↑ ~1:9 ↑ ~7:1
  • 14.
  • 15. Risk Difference (RD) Death No death Actual difference in risk of events Placebo Drug 133 148 RD = 14/133 – 128/148 = 0.11 – 0.86 = - 0.75 20 128 119 14
  • 16.
  • 17.
  • 18.
  • 19.
  • 21.
  • 22.
  • 23. Fixed Effects Analysis in Picture View
  • 24. Random Effects Analysis in Picture View
  • 25. Random effects in RevMan 5 ← DerSimonian and Laird random effects model
  • 26. Random effects in RevMan 5 (continued) ← DerSimonian and Laird random effects model
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36. I 2 Statistic (continued) * Importance of I 2 value depends on: ● magnitude and direction of effects ● strength of evidence of heterogeneity - Chi-squared P value, or - I 2 confidence interval Considerable heterogeneity* 75% to 100% May represent substantial heterogeneity* 50% to 90% May represent moderate heterogeneity* 30% to 60% Might not be important 0% to 40% Guide to Interpretation I 2 value
  • 37. Sample Forest Plot: Q and I 2
  • 38.
  • 39.
  • 40.
  • 42.