meta-analysis 統合分析 蔡崇弘

EBM ( evidence based medicine) Ask Acquire Appraising Apply Audit

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Finding relevant studies Different databases Unpublished primary studies search relevant databases writing to experts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 ： % 男生 ： % 女生 ： % 女生 ： % 總和 ： % 總和 ： % 總報名人數 總錄取人數 總錄取率 男生： % 女生： %

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/d 2 (d 2 variance of studies )

Random-effect model True value varies Weighs are 1/(d 2 + Tau 2 ) 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