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Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH
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Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

Jan 02, 2016

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Page 1: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

Kirsten Fiest, PhD

June 23, 2015

1

CONDUCTING META-ANALYSES IN HEALTH RESEARCH

Page 2: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

A statistical method of combining data from multiple independent sources

Powerful tool to compare the effects of interventions, determine the magnitude of association, or prevalence/incidence of disease

Often informed by the results of a systematic review

Can assess differences between subgroups that may not be possible in individual studies

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META-ANALYSIS

Page 3: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

SAS, STATA, SPSS, R, spreadsheets, RevMan

Graphics quality will differ

Easiest to start with data in a spreadsheet

Will need, at minimum, study identifier, effect size, and measure of error

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STATISTICAL PROGRAMS

Page 4: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

Fixed effect Assumes there is one true effect size to be estimated Pooled estimate is the common effect size Weighting is based entirely on the size of the study Only source of error is within studies

Random effects Allows the true effect to vary from study to study Trying to estimate the mean of a distribution of true

effects Weights assigned are more balanced Can be error within and between studies

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ANALYTICAL METHODS

Page 5: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

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INTERPRETING META-ANALYTIC OUTPUT

Random-Effects Model (k = 11; tau^2 estimator: REML) 

I^2 (total heterogeneity / total variability): 99.69% 

Test for Heterogeneity: Q(df = 10) = 3990.9717, p-val < .0001

 Model Results:

 estimate se zval pval ci.lb ci.ub

-3.0934 0.9093 -3.4018 0.0007 -4.8757 -1.3111

Page 6: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

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INTERPRETING A FOREST PLOT

Page 7: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

Clinical and statistical heterogeneity should be assessed Clinical heterogeneity

Factors known to influence the relationship under consideration

Eg. disease duration, age, sex

Statistical heterogeneity Measured most commonly by the I2 and Q statistics Assesses whether any observed differences may be due

to chance alone Interpret with caution (power)

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HETEROGENEITY

Page 8: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

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STRATIFICATION

Page 9: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

Potential bias for journals to publish large studies with significant results

Statistical tests to determine its presence Funnel plots Examine visually and

statistically Begg’s test is a rank

correlation method Egger’s test is a

regression-based method Trim and fill

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PUBLICATION BIAS

Page 10: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

Used to identify trends across an extraneous variable

Allows for the inclusion of continuous or categorical variables

Is the incidence of dementia changing over time?

Does the prevalence of epilepsy differ by geographic region?

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META-REGRESSION

Page 11: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

Method of comparing treatment effects

Pool data from multiple studies with one common arm

Can assess direct and indirect effects

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NETWORK META-ANALYSES

ACT, behavioural activation; CBT, cognitive-behavioural therapy; DYN, psychodynamic therapy; IPT, interpersonal therapy; PLA, placebo; PST, problem solving therapy; SST, social skills training; SUP, supportive counselling; UC, usual care; WL, waitlist.

Barth et al., PLOS Med, 2013, 10(5)

Page 12: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)

MOOSE (Meta-Analysis of Observational Studies in Epidemiology)

Consider reporting guidelines for initial studies as well (STARD, STROBE, CONSORT)

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REPORTING GUIDELINES

Page 13: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

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Page 14: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

14Patten et al.; CJP, 2014, 59(11):60-614

Page 15: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

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Page 16: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

Limited by the reporting of individual papers Definitions, estimates provided, basic study details

Quality of individuals studies may vary

Heterogeneity between estimates may weaken some conclusions

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LIMITATIONS

Page 17: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

Reporting guidelines

Systematic Reviews in Health Care: Meta-Analyses in Context, 2nd Edition. Egger, Smith & Altman. 2008.

Journals in your field of interest

RESOURCES

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Page 18: Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH.

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