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Different databases Unpublished primary studies search relevant databases writing to experts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