meta-analysis 統合分析 蔡崇弘
EBM ( evidence based medicine)AskAcquireAppraisingApplyAudit
FIRE
Formulate an answerable question Information search Review of information and clinical
appraisal Employ the result in clinical practice
question
Intervention Frequency or rate Diagnostic accuracy Risk or etiology Prediction and prognosis
Finding relevant studies
Existing systematic reviews Published primary studies Breaking down study question into
components Synonyms Snowballing Handsearching Methodological terms Methodological filters
Finding relevant studies
Different databases Unpublished primary studies search relevant databases writing to experts
Appraising and selecting studies VIP Jadad score
review
Narrative reviews Systematic review
paper
One question, one paper One question, more papers More papers to get one conclusion. Different paper, different effect 7 ( 4 effect 3 non-effect) Conclusion!
Vote counting
P value ( P>0.05, P<0.05) Is it good for you?
Meta-analysis
Systematic review and meta-analysis
Meta-analysis is used in many fields of research
Meta-analysis as part of the research process
Four stages of research synthesis Problem collection Data collection Data evaluation Data analysis and interpretation
Simpson’s paradox 法學院:報名人數 錄取人數 錄取率 商學院:報名人數 錄取人數 錄取率 男生 : 53 8 15.1% 男生 : 251 201 80.1% 女生 : 152 51 33.6% 女生 : 101 92 91.1% 總和 : 205 59 28.8% 總和 : 352 293 83.3%
總報名人數 總錄取人數 總錄取率 男生: 304 209 68.8% 女生: 253 143 56.5%
What does a meta-analysis entail?
Which comparisons should be made? Which study results should be used in
each comparison? What is the best summary of effect for
each comparison? Are the results of studies similar within
each comparison? How reliable are those summaries?
Types of data and effect measures
Dichototomous outcomes OR, RD, NNT. Continuous outcomes mean difference, standardised mean difference, Ordinal outcomes Counts and rates Time-to-event outcomes Log scales
How a meta-analysis work
Individual studies Effect size Precision Study weights P-values The summary effect Heterogeneity of effect sizes
software
Comprehensive meta-analysis Reman Stata macro with stata SAS R
Why perform a meta-analysis
Statistical significance Clinical importance of the effect Consistency of effects
Doing arithmetic with words The words are based on p-values the
words are the wrong words.
Effect size and precision
Treatment effects and effect sizes How to choose an effect size Parameters and estimates Outline of effects size computations
Factors that affect precision
Variance, standard error, and confidence intervals
Factors that affect precision Sample size Study design Concluding remarks
Fixed-effect model
A single true value Weighs are 1/d2 (d2 variance of
studies )
Random-effect model
True value varies Weighs are 1/(d2 + Tau2 ) Tau= study- to study variation
If between study variance is small then fixed and random effects models are similar.
If the between study variance is large, the weighs for each study become almost equal.
Minimal between study variation- the choice doesn’t matter.
Considerable between study variation then an explanation should be sought.
Identifying and quantifying heterogeneity Isolating the varivation in true effects Computing Q The expected value of Q based on
within-study error The excess variation Ratio of observed to expected variation Testing the assumption of homogeneity
in effects Concluding remarks about Q and p-
value
Estimating Concluding remarks about T Tau Concluding remark about T The I statising Comparing the measures of
heterogeneity Confidence interval for
Prediction intervals
Prediction intervals in primary studies Prediction intervals in meta-analysis Confidence intervals and prediction
intervals Comparing the confidence interval with
the prediction interval
Subgroup analysis
Fixed-effect model within subgroups Computing the summary effects Computation for A studies Computation for B studies Computations for all ten studies Comparing the effect
Meta-regression
Fixed-effect model Assessing the impact of the slope The Z-test and Q-test Quantify the magnitude of the
relationship Fixed or random effects for
unexplained heterogeneity The proportion of variance explained
Publication bias
The problem of missing studies Studies with significant results are
more likely to be published Published studies are more likely to be
included in a meta-analysis Other sources of bias Methods for addressing bias.
Generally of the basic inverse-variance method Other effect sizes Simple descriptive statistics Physical contents Two-group studies with other types of
data Three-group studies Regression coefficients
Further methods for dichotomous data Mantel-Haenszel method Peto odds ratio method Dersimonnian and Laird random
effects method
When does it make sense to perform a meta-analysis? Are the studies similar enough to combine? 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? How many studies are enough to carry out a
meta-analysis?
Reporting the results of a meta-analysis Are the effects consistent? The computational model Forest plots Sensitivity analysis
Cumulative meta-analysis
Why perform a cumulative meta-analysis?
Cumulative meta-analysis as an educational tool
To identify patterns in the data c Display, not analysis Using a cumulative analysis
prospectively
Criticisms of meta-analysis 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
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