演講-meta analysis in medical research-張偉豪
TRANSCRIPT
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張偉豪
三星統計服務有限公司 執行長
SEM 亞洲一哥
統計黑傑克
版次:20151225
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If you can't explain it simply, you don't understand it well enough.
Albert Einstein
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What is Meta-Analysis
Software of Meta-Analysis
How to plan a Meta-Analysis
RCT, Cohort study or Case study
Effect size
Risk ratio(RR) vs. Odds ratio(OR)
Fix effect vs. Random effect
Heterogeneity test
Publication bias
Reporting the results of a Meta-Analysis
Outline
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Books
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Meta-analysis is a quantitativeapproach for systematically combining results of previous research to arrive at conclusions about the body of research.
What is Meta-Analysis
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Quantitative : numbers
Systematic : methodical
Combining: putting together (mean and variance)
Previous research: what's already done
Conclusions: new knowledge
What is Meta-Analysis
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When individual trials or studies’ sample sizes
are too small to give reliable answers.
When large trials or studies are impractical or impossible
Potentially lead to more timely introduction of effective treatment
When there have been many trials or studies showing small effects may be important.
Avoid institutional review board (IRB) censor.
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Advantages of Use of Meta-Analysis
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Hierarchy of evidence
Meta-Analysis
Systematic Review
Randomized Controlled Trial
Cohort studies
Case Control studies
Case Series/Case Reports
Animal research
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Individual studies
Collecting similarity studies from previous research.
Effect sizes (ES)
Transform data (analysis results) into effect size to reflect the magnitude of treatment effect or the strength of a relationship between two variables.
Precision
The effect size for each study is bounded by as confidence interval (CI), reflect the precision of effect size.
How a Meta-Analysis work
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Study weight
Ideal studies (sample size are larger) are assigned relatively high weight.
P-value
A p-value for a test of the null hypothesis.
If p<0.05 reject null hypothesis.
The summary effect
Summary the effect size from all studies, including mean ES (fix effect), CI, weight, p-value, ES heterogeneity, random effect, publication bias etc..
How a Meta-Analysis work
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CMA is able to accept data in more
than 100 formats and allows the user to mix and match formats in the same analysis.
CMA is able to perform fixed-effect and random-effects analyses. They all report the key statistics, such as the summary effect and confidence intervals, measures of heterogeneity (T2, Q, I2)
CMA allow the researcher to automate the process, performing the analysis repeatedly and removing a different study on each pass.
Why use Comprehensive Meta-Analysis (CMA)
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Why use Comprehensive Meta-Analysis (CMA)
CMA allows the user to define a hierarchical structure and then offers the user a set of options including the option to create a synthetic variable based on some (or all) the outcomes, or to work with each outcome separately.
CMA offer a full set of tools to assess publication bias.
CMA support 50 formats for data entry, all of the basic computational options, and high-resolution forest plots.
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Define the Research Question
Perform the literature
search
Determine eligibility of
studies
Extract the data from
studies
Analyze the data in the
study statistically
Examine heterogeneity
Assess publication
bias
Interpret and Report the
results
Steps in a meta-analysis
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Eight Steps of Meta Analysis
1. Define the Research Question
2. Perform the literature search
3. Determine eligibility of studies
Inclusion: which ones to keep
Exclusion: which ones to throw out
4. Extract the data from studies
5. Analyze the data in the study statistically
6. Examine heterogeneity
7. Assess publication bias
8. Interpret and Report the results
How to plan a Meta-Analysis
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In patients with coronary artery disease (CAD)
does vitamin E supplementation decrease the risk of death?
Patients digest Carotenoids will decrease the chance of lung cancer happen.
Define the Research Question
Define the Research Question
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Potentially relevant references identified after liberal screening of the electronic search (n=#)
Excluded by Title/Abstract (n=#) List the reasons
Articles retrieved for more detailed evaluation (n=#)
Articles excluded after evaluation of full text (n=#) List the reasons
Relevant studies included in the meta-analysis (n=#)
Flow Diagram of Study Selection Process
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Be methodical: plan first
List of popular databases to search
Pubmed/Medline/Embase
List every possible database you may search.
Other strategies you may adopt
Hand search (go to the library...)
Personal references, and emails
web, eg. Google scholar (http://scholar.google.com)
Identify your studies
Perform the literature
search
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Let's say we want to know that passive smoking really cause lung cancer.
How should we set up a search strategy?
What is the key words?
“Smoking” or/and “lung cancer”
Passive/Second hand smoking
Active smoking
Air pollution
Lung disease
Search key word
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“passive smoking” OR “second hand smoking”[text word] OR lung cancer produces ALL articles that contain EITHERsmoking OR lung cancer to get a lot of articles.
“Passive smoking” AND “lung cancer” will capture only those subsets that have BOTH smoking AND lung cancer reduce the articles.
The Search
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Cannot include all studies
Keep the ones with
high levels of evidence
good quality
Usually, MA done with RCTs
Case series, and case reports definitely out
Selection problems are major problems
Keep some, throw out others
Determine eligibility of
studies
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Are the studies similar enough to combine?
Can I combine studies with different designs?
Experiential VS. Observational
Studies that used independent groups, paired groups, clustered groups
Can I combine studies that report results in different ways?
How many studies are enough to carry out a meta-analysis?
When Does it Make Sense to Perform a Meta-Analysis?
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Randomized Controlled Trials (RCTs)
• The cases who was random select from population
• Belong to experimental study
• Exposure didn’t naturally
• Blind randomized trial
RCT, Cohort study or Case study
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Cohort Study is any group of people who are
linked in some way and followed over time.
Belong to observational study
Expose naturally in nature world
Prospective Cohort study
Retrospective Cohort study
Time Series Study
Case Control
examine associations between disease/disorder/health issue and one or more risk factors
RCT, Cohort study or Case study
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Question: Will smoke behavior cause lung cancer?
Prospective Cohort study
Causality research
Find multiple consequence
Retrospective Cohort study
Find multiple causes may cause diseases
Outcome is determined before exposure status
No need huge sample size
Cohort study
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Researchers use existing records to identify
people with a certain health problem (“cases”) and a similar group without the problem (“controls”).
Similar retrospective Cohort study
Example: To learn whether a certain drug causes birth defects, one might collect data about children with defects (cases) and about those without defects (controls).
The data are compared to see whether cases are more likely than controls to have mothers who took the drug during pregnancy.
Case control study
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Create a spreadsheet (Excel, or OpenOffice Calc)
For each study, create the following columns:
name of the study
name of the author, year published
number of participants who received intervention
number of participants who were in control
number who developed outcomes in intervention
number who developed outcomes in control
How to Abstract Data
Extract the data from
studies
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Spreadsheet Data for Strepto Study
We created seven columns
trial: trial identity code
trialname: name of trial
year: year of the study
pop1: study population
deaths1: deaths in study
pop0: control population
deaths0: deaths in control
There are 22 studies to do our meta analysis
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Data entry
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The properties of effect size in a
meta-analysis
be comparable across studies (standardization)
represent magnitude & direction of the relationship
be independent of sample size
Effect size
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The ES makes meta-analysis possible
The ES encodes the selected research findings on a numeric scale
There are many different types of ES measures, each suited to different research situations
Each ES type may also have multiple methods of computation
Effect size (ES)
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Standardized mean difference
Group contrast research
Cohen’s d = 02, 0.5, and 0.8 as a small, medium, and large effect size
Output is continuous.
Odds-ratio
Group contrast research
OR = 1.68, 3.47, and 6.71 as a small, medium, and large effect size
Output is dichotomous.
Correlation coefficient
Association between variables research31
Different Types of Effect Sizes
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Odds definition The probability of event divided by the probability of
the alternative.
Odds = p/1-p
𝑶𝑹 =𝑶𝒅𝒅𝒔 𝒐𝒇𝒆𝒙𝒑𝒐𝒔𝒖𝒓𝒆 𝒊𝒏 𝒕𝒉𝒐𝒔𝒆 𝒘𝒊𝒕𝒉 𝒅𝒊𝒔𝒆𝒂𝒔𝒆
𝑶𝒅𝒅𝒔 𝒐𝒇𝒆𝒙𝒑𝒐𝒔𝒖𝒓𝒆 𝒊𝒏 𝒕𝒉𝒐𝒔𝒆𝒘𝒊𝒕𝒉𝒐𝒖𝒕 𝒅𝒊𝒔𝒆𝒂𝒔𝒆
Interpretation
OR>1 Increase frequency of exposure among cases
OR=1 No change in frequency of exposure
OR<1 Decrease frequency of exposure
An OR about 2 is usually important
Odds ratio(OR)
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Definition of RR
The proportion experiencing the event in one group divided by the proportion experiencing it in the other.
RR = p1/p2
𝑹𝑹 =𝑰𝒏𝒄𝒊𝒅 𝒐𝒇 𝒐𝒖𝒕𝒄𝒐𝒎𝒆 𝒘𝒊𝒕𝒉 𝒆𝒙𝒑𝒐𝒔𝒖𝒓𝒆
𝑰𝒏𝒄𝒊𝒅 𝒐𝒇 𝒐𝒖𝒕𝒄𝒐𝒎𝒘𝒊𝒕𝒉𝒐𝒖𝒕 𝒆𝒙𝒑𝒐𝒔𝒖𝒓𝒆
RR is suitable Cohort studies
Interpretation
RR>1 Increase risk of outcome
RR=1 No risk of outcome
RR<1 Reduce risk of outcome
Risk ratio(RR)
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Fix effect
Assumes that all studies are estimating the same true effect
Variability only from sampling of people within each study
Precision depends mainly on study size
Fix vs. Random effect
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Random effect
Studies allowed to have different underlying or true effects
Allows variation between studies as well as within studies
Fix vs. Random effect
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Random effects generally yield larger variances
and CI
Why? Incorporate
If heterogeneity between studies is large between variance, will dominate the weights and all studies will be weighted more equally
Model weight for large studies less in random vs. fixed effects model
Fix vs. Random effect
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Statistical test for heterogeneity
Visual inspection/Graphical approach
Forest plot
Meta-regression
Unit of regression: study
Dependent variable: study-specific effect estimate
Independent variables: study-specific characteristics (e.g., study design, geographic location, length of follow-up)
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Examining Heterogeneity
Examine heterogeneity
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Different study designs
Different incidence rates among unexposed
Different length of follow-up
Different distributions of effect modifiers
Different statistical methods/models used
Different sources of bias
Study quality
Sources of Between Study Heterogeneity
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Examining Forest Plot for Heterogeneity
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The I2 statistic describes the percentage of variation across studies that is due to heterogeneity rather than chance. .
I2 statistic value is a standardized value.I2 statistic (between variance/total variance)
1. 0% ~ 40%: heterogeneity might not be important;
2. 30% ~ 60%: may represent moderate heterogeneity;
3. 50% ~ 90%: may represent substantial heterogeneity;
4. 75% ~ 100%: considerable heterogeneity.
Heterogeneity test
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In traditional (fixed-effects) meta-analysis heterogeneity test using the Q statistic.
The test has low power, so you use p<0.10 rather than p<0.05.
If p<0.10, you exclude "outlier" studies and re-test, until p>0.10.
When p>0.10, you declare the effect homogeneous.
Heterogeneity test
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Strategies for addressing heterogeneity
Check again that the data are correct
Do not do a meta-analysis
Explore heterogeneity (subgroup analysis, meta-regression)
Ignore heterogeneity (there is no an intervention effect but a distribution of intervention effects)
Perform a random-effects meta-analysis (when heterogeneity cannot be explained)
Change the effect measure (different scales in different studies)
Exclude studies (outlying studies)
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Sensitivity analysis
Sensitivity analysis have been used to examine the effects of studies identified as being aberrant concerning conduct or result, or being highly influential in the analysis.
One study removed meta-analysis
Cumulative analysis
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how the results would change if one study (or a
set of studies) was removed from the analysis.
One study removed meta-analysis
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A cumulative meta-analysis is performed first
with one study, then with two studies, and so on, until all relevant studies have been included in the analysis.
A cumulative analysis entering the larger studies at the top and adding the smaller studies at the bottom, sorted by sample size or precision.
A benefit of the cumulative analysis is that it displays not only if there is a shift in effect size, but also the magnitude of the shift.
Cumulative analysis
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What is Meta-Analysis bias?
Can bias the results of a meta-analysis toward a positive finding
Can evaluate publication bias graphically (funnel plot) or through statistical analysis
Test of Publication Bias
Assess publication bias
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Outcome reporting bias
Significant outcomes are more likely to be reported than non-significant outcomes.
Should unpublished data be included in systemic review?
Pre-specified inclusion (quality) criteria are recommended.
Database Bias
No single database is likely to contain all published
studies on a given subject.”
Where Can Publication Bias Occur?
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Publication Bias
selective publication of articles that show positive
treatment of effects and statistical significance.
English-language (duplication) bias
Studies with statistically significant results are more likely
to be published in English
Citation bias
occurs when studies with significant or positive results are
referenced in other publications, compared with studies
with inconclusive or negative findings
Meta-Analysis bias
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Funnel plot
Rosenthal’s Fail-safe N
Orwin’s Fail-safe N
Duval and Tweedie’s Trim & Fill
rank correlation (P>0.05)
Regression
Methods for addressing publication bias
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Funnel plot has several caveats:
1. funnel plot may yield a very different picture depending on the index used in the analysis (risk difference versus risk ratio).
2. Funnel plot makes sense only if there is a reasonable amount of dispersion in the sample sizes and a reasonable number of studies.
3. even when these criteria are met, the tests tend to have lower power.
Funnel plot
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The absence of a significant correlation or
regression cannot be taken as evidence of symmetry.
To solve these problems, we use
Rosenthal’s Fail-safe N
Orwin’s Fail-safe N
Duval and Tweedie’s Trim and Fill
Funnel plot
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What is our best estimate of the unbiased effect
size?
Trim and fill procedure will tell you the answer, the method separate into trim and fill two steps.
Trim & fill
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Trim first
remove the most extreme small studies from the positive side of the funnel plot, re-computing the effect size at each iteration until the funnel plot is symmetric about the (new) effect size.
yields the adjusted effect size(unbiased summate ES).
Fill follow
adds the original studies back into the analysis, and imputes a mirror image for each.
to correct the ES variance.
Trim and Fill procedure
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The fail-safe N (Rosenthal, 1991) determines the
number of studies with an effect size of zero needed to lower the observed effect size to a specified (criterion) level.
The fail-safe N actually compute how many missing studies we would need to retrieve and incorporate in the analysis before the p-value became nonsignificant..
Rosenthal’s Fail-safe N (File drawer analysis)
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the Fail-safe N is 38, suggesting that there would
need to be nearly 40 studies with a mean risk ratio of 1.0 added to the analysis, the research will become statistically nonsignificant.
Rosenthal’s Fail-safe N
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Orwin’s method allows the researcher to
determine how many missing studies would bring the overall effect to a specified level other than zero.
it allows the researcher to specify the mean effect in the missing studies as some value other than zero.
Orwin’s Fail-safe N
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Begg and Mazumdarrank correlation
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Is there evidence of bias?
Egger’s regression
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Combine data to arrive at a summary, 3 measures
Effect Size (Odds Ratio or Risk Ratio or Correlations)
Variance with 95% Confidence Interval
Test of heterogeneity
Two Graphs
Forest Plot
Funnel Plot
Examine why the studies are heterogeneous
Examine publication bias.
Reporting the results
Interpret and Report the
results
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Meta-Analysis check list
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Are the studies similar enough to combine?
There is no restriction on the similarity of studies Based on the types of participants, interventions, or exposures.
Can I combine studies with different designs?
Randomized trials versus observational studies
Studies that used independent groups, paired groups, clustered groups
Can I combine studies that report results in different ways?
When Does it Make Sense to Performa Meta-Analysis?
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How many studies are enough to carry out a
meta-analysis?
Fix effect model
At least two studies, since a summary based on two or more studies yields a more precise estimate of the true effect than either study alone.
Random effect model
When Does it Make Sense to Performa Meta-Analysis?
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One number cannot summarize a research field
The file drawer problem invalidates meta-analysis
Mixing apples and oranges
Garbage in, garbage out
Important studies are ignored
Meta-analysis can disagree with randomized trials
Meta-analyses are performed poorly
Criticisms of Meta-Analysis
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