1. META-ANALYSIS: THE ART AND SCIENCE OF COMBINING INFORMATION Ora Paltiel, October 28, 2014

2. DEFINITIONS The statistical analysis of a large collection of results from individual studies for the purpose of integrating the findings A quantitative review and synthesis of results of related but independent studies “overview” “data pooling” “data synthesis” systematic review

3. “Meta “ Webster’s dictionary: a) occurring later than or in succession to b) situated behind or beyond c) change, transformation Examples: metaphysics, metamorphosis.

4. OVER 2 MILLION MEDICAL ARTICLES ARE PUBLISHED EACH YEAR. The Problem The findings of new studies not only “ differ from previously established truths but disagree with one another, often violently” -Morton Hunt, How Science Takes Stock, P.1

5. The Goal of Meta-Analysis: “Making Order of Scientific Chaos” Began as a tool in Social Sciences 21 citations in 1986 431 citations in 1991 more than 45000 today In Medicine – at first only RCTs Now – thousands of meta-analyses of observational studies

6. is a group of over 15,000 volunteers in more than 90 countries who review the effects of health care interventions tested in biomedical randomized controlled trialsrandomized controlled trials reviews have also studied the results of non-randomized observational studies.observational studies The results of these systematic reviews published as "Cochrane Reviews" in the Cochrane Librarysystematic reviewsCochrane Library Founded in 1993 under the leadership of Iain Chalmers.Iain Chalmers developed in response to Archie Cochrane's call for up-to-date, systematic reviews of all relevant randomized controlled trials of health care.Archie Cochrane

7. Cochrane collaboration Goal : to help people make well informed decisions about health care by preparing, maintaining and ensuring the accessibility of systematic reviews of the effects of health care interventions. The principles of the Cochrane Collaboration are: collaboration building on the enthusiasm of individuals avoiding duplication minimizing bias keeping up to date striving for relevance promoting access ensuring quality continuity enabling wide participation

8. Major goals of Meta-Analysis Objective summaries Increase power to detect true effects Estimate effect size Resolve uncertainty Explore heterogeneity and reasons for it If the studies produced dissimilar results, How did they differ? Why? Study design, quality, populations, subtle intervention differences etc Tool for conducting evidence-based medicine and for setting policy

9. How to do a Meta-Analysis 1. Define research question, including intervention, population, and outcome to be assessed 2. Define eligibility criteria (types of study, design) 3. Identify all studies (published or un) which deal with the specified problem 4. Evaluate each article for inclusion or exclusion, on the basis of predefined criteria 5. Summarize, numerically, the results of these studies 6. Interpret these findings, with emphasis on explaining differences as well as summarizing the data

10. Literature review A comprehensive, systematic literature review should be conducted Sources: citation indexes, abstract databases, clinical trials registers, references, Issues: language, “grey literature”, conference abstracts, unpublished findings Meta-analysis is research, which should be reproducible, methods incl key words must be able to be replicated publication bias Problem of publication bias

11. SEARCH STRATEGY- example Horvath et al BMJ 2010;340:c1395

12. Information Assembled The report ( author, year) The study (population) The patients (demographic and clinical characteristics) The design The treatment The effect size ( estimate, SE) Methods, reliability and validity of recording information need to be documented

13. “Head-Counting - Statistical”: Count the number of significant results in each direction Result: 6 favor treatment, 0 favor placebo, 27 nonsignificant “Head-Counting”: Count the direction of the results in the studies Result: 24 favor treatment, 9 favor placebo Thirty three trials of streptokinase vs. conventional treatment for Acute Myocardial Infarction

15. Streptokinase - Summary Streptokinase reduces mortality by about 22% Efficacy proven by 2 large RCTs in 1986 and 1988 Meta-analysis proved efficacy in 1971 6380 lives could have been saved in large RCTs alone

16. What can we learn from the Forest Plot? Meta-analysis of gestational diabetes outcomes – 1. Maternal Horvath et al BMJ 2010;340:c1395

17. Statistical Methods We have a series of measures of association, one for each study We wish to summarize these measures This can be carried out using a weighted average of the estimates taken from each study.

18. Classic Meta-Analysis Analyzes RR, OR, or absolute differences in percentages between groups. Uses the the inverse of the variance of the estimate provided by each participating trial for the weights. This gives a minimum variance unbiased estimate of the effect. Large trials carry more weight than small trials.

19. Inference: fixed.v. random effects If interest is centered on making inferences for the populations that have been sampled, and we assume that there is a single effect of treatment - then a fixed effects approach is used. In this approach the only source of uncertainty is that resulting from sampling patients into the studies. Variation stems from within-study variation study. The population to which we wish to generalized the results consists of a set of studies having identical characteristics

20. Random-effects In random-effects approach the existing studies are considered as a random sample from a population of studies Random-effects approach is used when inferences are to be generalized to a population in which studies may differ in their effect and characteristics Random effects approach integrate also the between-study variability

21. Fixed vs. Random-effects The use of random-effects will produce somewhat larger 95% CI A good practice is to first perform a test of heterogeneity between studies. If no significant variation is found between studies - a fixed-effects approach can be used There are a number of ways to model random- effects

22. Heterogeneity Horvath et al BMJ 2010;340:c1395

26. Sensitivity analysis- comparators or control groups

27. Sensitivity analyses excluding studies with predefined less desirable characteristics, as follows: Risk of bias When the analysis was limited to two studies with a low risk of bias for random sequence generation and/or allocation concealment the add-on effect of acupuncture on patient-reported global assessment remained significant (RR 0.39, 95% CI 0.18–0.88, I 2 = 0%). Sample size When four studies with ≥ 40 participants per group were pooled, there was no significant difference in the risk of symptoms persisting or worsening between the acupuncture and control groups (RR 0.50, 95% CI 0.24–1.05, I 2 = 55%).

28. Assessing Quality A systematic approach should be used in order to assess the quality of the studies and to determine inclusion/exclusion of studies Explicit methods limit bias in identifying and rejecting studies Scales such as Jaddad scale

29. Domains to be assessed Methodological quality ( bias) Precision in estimation External validity

30. Assessing quality of included studies: -- RCTs- account in text

31. Assessment of bias, graphic representation

32. Risk of bias: Tabular presentation

33. Further Exploring Heterogeneity In case of substantial heterogeneity between studies, exploring its causes can be performed by considering covariates on the study level that could ‘explain’ differences between studies. Such analyses are called meta-regression

34. Meta-regression by study properties

35. Publication bias. Some studies are not published, selective presentation in those published. Do a comprehensive search. Use a funnel plot

36. Publication bias use of the funnel plot 1-SAMPLE SiZE

37. SAMPLE SiZE

38. Conclusions In times of increasing amount of information-a systematic approach to synthesizing information has many advantages. A systematic approach enables exploring heterogeneity between studies As any other type of research systematic review should be carried out methodically and cautiously

39. Problems with Meta-Analysis in Real Life “Meta-analysis” often not done, or very few studies combined Retrospective study Publication Bias Heterogeneity

40. Future Expect to see lots of meta-analyses Good ones and bad ones Scientific community will decide whether it is useful Be skeptical of everything

41. Supplementary material

44. Fixed versus Random effects

45. Robustness of results-meta-regression Meta-regressions are similar in essence to simple regressions, in which an outcome variable is predicted according to the values of one or more explanatory variables. In meta-regression, the outcome variable is the effect estimate (for example, a mean difference, a risk difference, a log odds ratio or a log risk ratio). The explanatory variables are characteristics of studies that might influence the size of intervention effect an investigation of how a categorical study characteristic is associated with the intervention effects in the meta- analysis. For example, studies in which allocation sequence concealment was adequate may yield different results from those in which it was inadequate. Here, allocation sequence concealment, adequate /inadequate, is a categorical characteristic at the study level. MR in principle allows the effects of multiple factors to be investigated simultaneously (although this is rarely possible due to inadequate numbers of studies) (Thompson 2002). Meta- regression should generally not be considered when there are fewer than ten studies in a meta-analysis.