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

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Presentation on theme: "Kirsten Fiest, PhD June 23, 2015 1 CONDUCTING META-ANALYSES IN HEALTH RESEARCH."— Presentation transcript:

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

2  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 2 META-ANALYSIS

3  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 3 STATISTICAL PROGRAMS

4  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 4 ANALYTICAL METHODS

5 5 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

6 6 INTERPRETING A FOREST PLOT

7  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 I 2 and Q statistics  Assesses whether any observed differences may be due to chance alone  Interpret with caution (power) 7 HETEROGENEITY

8 8 STRATIFICATION

9  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 9 PUBLICATION BIAS

10  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? 10 META-REGRESSION

11  Method of comparing treatment effects  Pool data from multiple studies with one common arm  Can assess direct and indirect effects 11 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)

12  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) 12 REPORTING GUIDELINES

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

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16  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 16 LIMITATIONS

17  Reporting guidelines  Systematic Reviews in Health Care: Meta-Analyses in Context, 2 nd Edition. Egger, Smith & Altman. 2008.  Journals in your field of interest RESOURCES 17

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