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- 1. META-ANALYSIS SPEAKER:- PREETI RAI TEACHER I/C:- DR. B. L VERMA DATE:- 12/11/2013
- 2. USE OF MOBILE PHONES LEAD TO NEUROMASGERMANY DRINKING RED WINE IS BENEFICIAL TO HEALTH-US STUDY CHOCOLATES ARE HARMFUL SOFT DRINKS ARE SAFE SOFT DRINKS CONTAIN HARMFUL PESTICIDES RED WINE IS HARMFUL-US CHOCOLATES ARE GOOD FOR HEART MOBILE PHONES CAUSE NO HARMEUROPE
- 3. WHAT IS THE ANSWER ?? HOW TO TAKE DECISION ??
- 4. Meta-analysis A meta-analysis is a quantitative study wherein a set of statistical procedure is used to summarize and synthesize results of a number of independently conducted research studies. If done well it can be very valuable to a researchers , because it provides an extensive bibliography of existing research on a topic ,while also providing a combined analysis of a number of study. Meta-analysis is a very time consuming, understanding and usually conducted by a team of researchers.
- 5. Thus, Meta Analysis is A Quantitative approach For systematically combining the results of previous researches to arrive at conclusions about the body of research.
- 6. What does it mean? Quantitative : numbers Systematic : methodical Combining : putting together Previous research : already done Conclusions : new
- 7. Important 1- MA falls under a broader classification of reviews known as Systematic Reviews 2- There are two types of SRs – (a) Qualitative (b) Quantitative (meta-analysis)
- 8. Important Both follow the same rigorous steps, EXCEPT that a qualitative review does not combine the endpoints for statistical analysis, usually because it’s not appropriate to combine them into any type of common Metric.
- 9. The Etymology "Meta" implies something occurring later, more comprehensive. Alternative terms are less specific — for example, "overview" is also used for traditional reviews, & "pooling" incorrectly implies that the source data are merged .
- 10. Historical notes Karl Pearson (1904) - Use of formal techniques to combine data from different samples. Glass (1976) coined the term metaanalysis
- 11. Rationale “by combining the samples of the individual studies, the overall sample size is increased, thereby improving the statistical power of the analysis as well as the precision of the estimates of treatment effects”
- 12. Objectives The benefits or hazards that might not be detected in small studies can be found . Integrating the findings. Identifying the treatment effect (or effect size) when it is consistent from one study to the next. Identifying the reason for the variation when the effect varies from one study to the next .
- 13. Importance MA Too much scientific information. A Well-informed clinical decision is difficult to reach, time consuming & cost-ineffective. Decisions about the utility of an intervention or the validity of a hypothesis can’t be based on results of a single study. Information from many studies with less effort & hassle .
- 14. Advantages of MA Saves effort and time Increases sample size – Gain in statistical power by reducing Random errors. Enhances reliability (precision) & accuracy (validity). Explores & Reduces bias.
- 15. Resolves controversies &conflicting reports Identifies crucial areas & questions that have not been adequately addressed with past research. Generalizes study results. May explain heterogeneity & its sources between the results of individual studies.
- 16. Answers questions about whether an overall study result varies among subgroups—for example, among men and women, older and younger patients, or subjects with different degrees of severity of disease. Reproducible numerical values – no place of unhelpful descriptors such as "no relation," "some evidence of a trend”, "a weak relation," and "a
- 17. Applications OF M A Clinicians & applied researchers - to determine which interventions work, and which ones work best. Basic research - to evaluate the evidence in diverse areas. Planning new studies. Some funding agencies now require it as part of the grant application to fund new research.
- 18. CAUTION Not used or meaningless, when – 1. Studies are different in terms of their population, intervention or how outcomes were measured. 2. Treatments, evaluated in the individual are different. studies 3. The findings of individual studies differ significantly – because combining widely differing results to produce an average effect would fail to represent the great variation in the outcomes .
- 19. Types of MA 1. Literature-based MA (LBMA) - most frequent type of MA - may be misleading as Data extraction & analysis may be less accurate 2. Individual patient data MA (IPDMA) - Gold-standard, but has problems - inability of investigators to supply data - the increased costs
- 20. Principles of MA 1-The need to consider the totality of evidence. 2-Requirement for Reproducibility Transparent, explicit & systematic approach. 3-Principles of reliable detection of the effects of health care interventions
- 21. The Process The process simply involves: 1. Calculation of the treatment effect i.e. OR/RR. 2. Calculation of the 95% Confidence interval around the individual OR/RR. 3. Giving a weight to the individual OR/RR (shown as the size of the box in the forest plot). The weight is calculated as the inverse of the square of the standard error of each OR/RR (1/SE2).
- 22. How do we conduct MA 1. 2. 3. 4. 5. 6. 7. Write a protocol – the blueprint Locating relevant studies Selecting & appraising studies for inclusion Data extraction from selected studies Statistical methods to combine the effect measures extracted from primary studies Addressing biases and limitations Results in graphical form – the Forest Plot
- 23. How do we conduct MA 1. 2. 3. 4. 5. 6. 7. Write a protocol – the blueprint Locating relevant studies Selecting & appraising studies for inclusion Data extraction from selected studies Statistical methods to combine the effect measures extracted from primary studies Addressing biases and limitations Results in graphical form – the Forest Plot
- 24. Recipe of a good protocol 1. 2. 3. 4. 5. 6. 7. 8. Purpose of meta-analysis. Design a research question. Search for studies. Specify study selection (inclusion & exclusion) & appraisal criteria. Decide data extraction procedures (including statistical reanalysis). Select an analytical strategy (use of models & sensitivity analysis). Anticipate systematic errors (biases)/limitations. Present & disseminate results .
- 25. How do we conduct MA 1. 2. 3. 4. 5. 6. 7. Write a protocol – the blueprint Locating relevant studies Selecting & appraising studies for inclusion Data extraction from selected studies Statistical methods to combine the effect measures extracted from primary studies Addressing biases and limitations Results in graphical form – the Forest Plot
- 26. Locating relevant studies Systematic approach. Primary objective – Strategically locate as much of the completed research on the topic as possible. Document strategy in sufficient detail to allow others to critique it’s quality. Usually include e-databases (MEDLINE, CINAHL, Psyclit, Embase, Cochrane Library) and others
- 27. How do we conduct MA 1. 2. 3. 4. 5. 6. 7. Write a protocol – the blueprint. Locating relevant studies. Selecting & appraising studies for inclusion . Data extraction from selected studies. Statistical methods to combine the effect measures extracted from primary studies Addressing biases and limitations. Results in graphical form – the Forest Plot.
- 28. Selecting & appraising studies for inclusion Selecting - Judge the relevance of the studies to the review question. Appraising - Judge numerous features of design & analysis . Methodical, impartial and reliable strategies are necessary as MA are retrospective exercises & are therefore susceptible to both random & systematic sampling errors . Rationale - by excluding lesser quality studies the risk of error/bias will be lessened.
- 29. How do we conduct MA 1. 2. 3. 4. 5. 6. 7. Write a protocol – the blueprint. Locating relevant studies. Selecting & appraising studies for inclusion. Data extraction from selected studies. Statistical methods to combine the effect measures extracted from primary studies. Addressing biases and limitations. Results in graphical form – the Forest Plot.
- 30. Data extraction Eligibility criteria for inclusion of data . Data collection in standardized record form. 2 independent observers extract the data, to avoid errors. Blinding observers to the names of the authors, their institutions, the names of the journals, sources of funding, and acknowledgments leads to more consistent scores.
- 31. How to Extract Data Create a spreadsheet (Excel) For each study, create the following columns: name of the study name of the author, year published number of participants who received intervention number of participants who were in control arm number who developed outcomes in intervention number who developed outcomes in control arm
- 32. We got like 22 studies to do our meta analysis, after all
- 33. How do we conduct MA 1. 2. 3. 4. 5. 6. 7. Write a protocol – the blueprint Locating relevant studies Selecting & appraising studies for inclusion Data extraction from selected studies Statistical methods to combine the effect measures extracted from primary studies Addressing biases and limitations Results in graphical form – the Forest Plot
- 34. Statistical Methods They attempt to answer basic questions: (a) Are results of different studies similar? -Check for heterogeneity. (b) To what extent that they are similar? -Calculate the amount of heterogeneity. (c) What is the best overall estimate? - Combine the effect measures using suitable model & calculate the summary effect size & its CI. (d) How precise & robust is this estimate? - Do Sensitivity Analysis. (e) Finally, can dissimilarities be explained?
- 35. Heterogeneity if present, should not simply be ignored after a statistical test is applied; rather, it should be scrutinized and explained. More weight is given to – (a) larger trials (b) Studies with narrow CI
- 36. • Assess the heterogeneity of effect size across the studies • Decide the type of model for combining the effect size of all studies. • 2 models to adjust the potential confounding effects of study – (1) Fixed Effect model . (When the combined trials are a homogeneous set) (2) Random Effect model. (When heterogeneity is detected)
- 37. How do we conduct MA 1. 2. 3. 4. 5. 6. 7. Write a protocol – the blueprint. Locating relevant studies. Selecting & appraising studies for inclusion. Data extraction from selected studies. Statistical methods to combine the effect measures extracted from primary studies. Addressing biases and limitations . Results in graphical form – the Forest Plot.
- 38. BIASES & LIMITATIONS Publication bias Selection bias Data extraction bias Database bias Citation Bias Data provision Bias
- 39. Publication Bias Synthesis of published data can yield an exaggerated effect as studies that yield relatively large/beneficial treatment are more likely to publish . English language bias, citation bias, & multiple publication bias- In English ,studies are more likely to be cited, and more likely to be published repeatedly.
- 40. High likelihood of publishing – - Studies sponsored by government or NGO. - Multi-centric studies. Many authors may not submit studies with negative findings because they anticipate rejection.
- 41. Other Biases Selection/Inclusion Bias-Manipulation of the inclusion criteria could lead to selective inclusion of studies with positive findings. Database bias – Selective inclusion of studies from developed countries. Citation bias – Ease of locating and contacting authors from reference lists. Data provision Bias – due to willingness of investigators to make their data available.
- 42. Testing for bias – Funnel Plot The presence of bias should be examined in sensitivity analyses and funnel plots. Funnel plot – It is graphical test for any type of bias that is associated with sample size. Results from small studies will scatter widely at the bottom of the graph. The spread will narrow as precision increases among larger studies. In the absence of bias, the plot should thus resemble a symmetrical inverted funnel. If the plot shows an asymmetrical & skewed shape, bias may be present .
- 43. Funnel Plot: what & how to read To study a funnel plot, look at its LOWER LEFT corner, that’s where negative or null studies are located If EMPTY, this indicates “PUBLICATION BIAS” Note that here, the plot fits in a funnel, and that the left corner is not all that empty, but we cannot rule out publication bias
- 44. How do we conduct MA 1. 2. 3. 4. 5. 6. 7. Write a protocol – the blueprint. Locating relevant studies. Selecting & appraising studies for inclusion. Data extraction from selected studies. Statistical methods to combine the effect measures extracted from primary studies. Addressing biases and limitations Results in graphical form – the Forest Plot.
- 45. Forest plots Effective way of presenting results: Studies, effect sizes, confidence intervals Provides an overview of consistency of effects Summarizes an overall effect (with confidence interval) Useful visual model of a meta-analysis
- 46. Anatomy of a forest plot… Study effect size (with C.I.) N of study Line of no effect C.I Studies Weighting of study in metaanalysis Study effect size Pooled effect size Pooled effect size
- 47. When individual studies are inconclusive deficient or its not possible to do Multicentric RCT , Money problem ,Time nahi mila.
- 48. Weighting studies 56 More weight to the studies which give us more information More participants More events Lower variance Weight is closely related to the width of the study confidence interval: wider confidence interval = less weight
- 49. EFFECT OF β-BLOCKADE AFTER MI 1- 3+ SG1 = MIX POP 5+ SG3 = GERMAN POP 2+ SG2 = MIX POP 4+ SG5 = MIX POP SG4 = MIX POP META ANALYSIS BENEFICIAL EFFECT OF β-BLOCKADE 13+
- 50. Limitations of MA Can’t improve the quality or reporting of the original studies. Limitations arising from mis-applications: - when study diversity is ignored or mishandled , and - when variability of patient populations’, quality of data & potential for underlying biases are not addressed. Publication bias is a major limitation.
- 51. Some clinicians consider it as "a tool that has become a weapon” & which represents "the unacceptable face of statisticism" & "should be stifled at birth” At the other end of the spectrum, the application has been hailed “Newtonian”. Some reject and see it as "mega-silliness”
- 52. MA continues to be controversial technique ? The mixed reception is not surprising The pooling of results from a particular set of studies may be inappropriate from a clinical point of view, producing a population " average" effect. Meta-analyses of the same issue may reach opposite conclusions.
- 53. Still, Meta Analyses hold promise…. If original studies of the effects of clot busters after heart attacks had been systematically reviewed, the benefits of therapy would have been apparent as early as the mid-1970s. Traditional approaches were inadequate in summarizing the current state of knowledge & omitted mention of effective therapies.
- 54. Popularity of Meta Analyses 3000 2500 Number of Publications 2000 1500 1000 500 0 93-94 94-95 95-96 96-97 97-98 98-99 99-00 Year of Publications 2000-1 2001-2 2002-3 2003-4
- 55. Refrences Lipsey, M.W., Wilson, D. B. Practical meta-analysis. Thousand Oaks, CA Sage; (Applied Social Research Methods Series; 49), 2001. Petitti, D. B. Meta-analysis, decision analysis, and cost-effectiveness analysis: Methods for quantitative synthesis in medicine (2nd ed.). New York Oxford University Press; 2000. Sutton AJ, Abrams KR, Jones DR, Sheldon TA, Song F. Methods of meta-analysis in medical research. West Sussex, England Wiley, 2000. The Handbook of Research SynthesisHarris Cooper & L Hedges Statistical Methods for Meta-Analysis – L Hedges & I OlkinLike Practical Meta-Analysis - Mark Lipsey and David Systematic Reviews in Health Care: Meta-Analysis in context - M Egger, G Davey-Smith, D G Altman, Foreword by Iain Methods for Meta-Analysis in Medical Research- AJ Sutton, K R Abrams, DR Jones, TA Sheldon, F SongLike Meta-Analysis in Medicine and Health Policy - DK Stangl, DBerry Publication Bias in Meta-Analysis - H Rothstein, A Sutton, M Borenstein How Science Takes Stock: The Story of Meta-Analysis - Morton Methods of Meta-Analysis: Correcting Error and Bias in Research Findings - John E. Hunter and Frank L. Schmidt Synthesizing Research - A Guide for Literature Reviews - Harris Meta-Analysis of Controlled Trials - Anne Whitehead
- 56. THANK YOU

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