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6 sr and meta analysis-ayurved

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6 sr and meta analysis-ayurved

  1. 1. Systematic Reviews and Meta-analysis Dr Nazli Khatib (MD, PhD) Professor (Physiology) International Initiative Impact (3ie) Evaluation Awardee JNMC DMIMS(DU) 1
  2. 2. Objectives of the talk • To provide the needs, characteristics and constitution of systematic review • To provide an overview of the process of planning, undertaking and writing a systematic review • To provide an overview of meta-analysis 2
  3. 3. Levels of evidences in health care 3
  4. 4. The need for SR ?? • Healthcare providers, consumers & policy makers are inundated with loads of information. • No time, skills & resources to find, appraise and interpret this evidence and to incorporate it into healthcare decisions. • SR respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in an accessible format. 4
  5. 5. Systematic review 5
  6. 6. So, a systematic review..... • ...attempts to pool all pragmatic evidences in order to answer a specific research question. • ...uses precise, systematic methods that are selected with a view to minimizing bias, thus providing reliable findings from which conclusions can be drawn and decisions made (Antman 1992, Oxman 1993). 6
  7. 7. Key characteristics of an SR • An explicit methodology • A systematic search that identifies all studies • An assessment of the validity of the findings of the included studies • A systematic presentation, and synthesis of the characteristics and findings of the included studies. 7
  8. 8. Need for an SR.... SR can be conducted in an effort to: • Resolve conflicting evidence • Help people make practical decisions about health care. • Address questions where clinical practice is uncertain • Establish feasibility of an intervention • Summarize and help people to understand the evidence. • Confirm the appropriateness of current practice or highlight a need for future research. • Avoiding redundant unnecessary trials 8
  9. 9. SR with Meta-analysis • Meta-analysis is the use of statistical methods to summarize the results of independent studies. • Not every SR leads to a meta-analysis. But it is better if a meta-analysis is based on an underlying systematic review. 9
  10. 10. Before you start with a SR... • Need to make sure no other systematic review identical to yours has recently been conducted • Need to make sure there is a need for review 10
  11. 11. Steps in SR 1. Define the review question 2. Search for studies 3. Select studies 4. Extract data 5. Assess risk of bias in included studies 6. Analyse data and undertake meta-analyses 7. Address reporting biases 8. Present results and ‘Summary of findings’ tables 11
  12. 12. SR titles 12 Scenario Structure Example Basic structure. [Intervention] for [health problem]. Antibiotics for acute bronchitis. Comparing two active interventions. [Intervention A] versus [intervention B] for [health problem]. Immediate versus delayed treatment for cervical intraepithelial neoplasia. Type of people being studied or location of intervention mentioned explicitly. [Intervention] for [health problem] in [participant group/location]. Inhaled nitric oxide for respiratory failure in preterm infants
  13. 13. 1. Defining the review question • A clearly defined, focused review begins with a well framed question. • A well-formulated research question increase the efficiency of the review by limiting the time and cost of identifying and obtaining relevant literature. • The review question should be in PICO format. • SR can focus on broad questions, or be more narrowly defined. 13
  14. 14. Steps in SR 1. Define the review question 2. Search for studies 3. Select studies 4. Extract data 5. Assess risk of bias in included studies 6. Analyse data and undertake meta-analyses 7. Address reporting biases 8. Present results and ‘Summary of findings’ tables 14
  15. 15. 2. Searching for studies • Electronic databases • “Grey literature” ( thesis, Internal reports, pharmaceutical industry files) • Checking reference lists • Unpublished sources known to experts in the specialty (seek by personal communication and look in trial registers) • Hand searching • Has to be as comprehensive as possible 15
  16. 16. Steps in SR 1. Define the review question 2. Search for studies 3. Select studies 4. Extract data 5. Assess risk of bias in included studies 6. Analyse data and undertake meta-analyses 7. Address reporting biases 8. Present results and ‘Summary of findings’ tables 16
  17. 17. 3. Selecting studies (Inclusion and exclusion of studies) • Scan titles & abstracts to retrieve possibly relevant studies • Correspond with investigators, where appropriate, to clarify study • Decide if studies meet inclusion criteria and record reasons for exclusion • Done by two independent review authors 17
  18. 18. Selecting studies: Prisma chart
  19. 19. Steps in SR 1. Define the review question 2. Search for studies 3. Select studies 4. Extract data 5. Assess risk of bias in included studies 6. Analyse data and undertake meta-analyses 7. Address reporting biases 8. Present results and ‘Summary of findings’ tables 19
  20. 20. 4. Extracting data • Data needed from each study  Details of the trial  Numerical data of the outcomes in each group to enter into meta-analysis 20
  21. 21. 21
  22. 22. Steps in SR 1. Define the review question 2. Search for studies 3. Select studies 4. Extract data 5. Assess risk of bias in included studies 6. Analyse data and undertake meta-analyses 7. Address reporting biases 8. Present results and ‘Summary of findings’ tables 22
  23. 23. 5. Assessing the risk of bias • If poor quality trials are the building blocks of a systematic review, the review may follow high quality methods, but the quality of evidence may still be poor • Use of poor quality methods may change the results of a trial • Inclusion of poor quality studies in meta-analysis is likely to exaggerate treatment effect • The evaluation of the validity of the included studies is therefore an essential. 23
  24. 24. Risk of bias 24
  25. 25. Steps in SR 1. Define the review question 2. Search for studies 3. Select studies 4. Extract data 5. Assess risk of bias in included studies 6. Analyse data and undertake meta-analyses 7. Address reporting biases 8. Present results and ‘Summary of findings’ tables 25
  26. 26. 6. Analyse data - Meta-analysis Principles • Patients in one trial are NEVER directly compared with those in another trial • Each trial is analysed separately • Summary statistics are collected /calculated for each trial • Each study is weighted • These summary statistics are added together to give the ‘meta-analysis’ 26
  27. 27. Why perform a meta-analysis? • Increase power • Improve precision of estimate • Quantify treatment effects and their uncertainty • Assess consistency of results • Answer questions not posed by individual studies • Settle controversies from conflicting studies • Generate new hypotheses 27
  28. 28. When can you do a meta-analysis? • When more than one study has estimated an effect • When characteristics of the studies are sufficiently similar so it makes sense to combine them • When the data are available in the form we need to combine them (e.g. outcome measured in similar ways) 28
  29. 29. When not to do a meta-analysis? • ‘Mixing apples with oranges’  Not useful for learning about apples  Useful for learning about fruit  Meta-analysis may be meaningless and genuine effects may be obscured if studies are too clinically diverse • Meta-analysis in presence of serious  publication and/or reporting biases may produce an inappropriate summary 29
  30. 30. Steps in doing a meta-analysis 1. Define comparisons for your review 2. Decide on outcomes for each comparison 3. Data retrieval 4. Entering and pooling data 5. Exploring heterogeneity 6. Interpreting results 30
  31. 31. Defining comparisons and outcome 31
  32. 32. Steps in doing a meta-analysis 1. Define comparisons for your review 2. Decide on outcomes for each comparison 3. Data retrieval 4. Entering and pooling data 5. Exploring heterogeneity 6. Interpreting results 32
  33. 33. Retrieving, entering & pooling data 33
  34. 34. Data analysis: Forest plot Forest plot of comparison: Ghrelin v/s Control; Outcome: Mortality 34
  35. 35. Steps in SR 1. Define the review question 2. Search for studies 3. Select studies 4. Extract data 5. Assess risk of bias in included studies 6. Analyse data and undertake meta-analyses 7. Address reporting biases 8. Present results and ‘Summary of findings’ tables 35
  36. 36. 8. Summary of findings table (SoF) • There are 3 key parts of a SoF table:  Information about the review  Summary of the statistical results  Grade of the quality of evidence. 36
  37. 37. Composition of a SR 37
  38. 38. Grants for SR • ICMR • 3ie’s Systematic Review Call 5 is funding 8 systematic reviews. • Department For International Development, UK Aid are commissioning 10 new SR on topics such as education, health, infrastructure and humanitarian assistance. • NIHR Cochrane Programme Grant Scheme • NIHR Cochrane Incentive Awards • USAID funded reviews 38
  39. 39. Grants for SR • 3ie, along with the Canadian International Development Agency (CIDA), the United States Agency for International Development (USAID), and the Millennium Challenge Corporation (MCC) is inviting proposals for SR in international development. This call will fund up to 15 review questions in the areas of agriculture, infrastructure, business development, governance and education interventions. 39
  40. 40. Summary • SR is “the application of scientific strategies that limit bias by the systematic assembly, critical appraisal and synthesis of all relevant studies on a specific topic.” (Cook 1995) • Help people to resolve conflicting evidence & make practical decisions about health care. • Steps in SR: Def RQ Search studies Select studies Extract data  Assess risk of bias Analyse data Address reporting biases Present results and ‘SoF’ tables Interpret results and draw conclusions 40
  41. 41. Summary... • Meta-analysis is the use of statistical methods to summarize the results of independent studies. • Steps in meta-analysis: Define comparisons & outcomes Data retrieval Entering and pooling data Exploring heterogeneity Interpreting results 41
  42. 42. To increase the power & precision 42
  43. 43. Generate new hypotheses 43
  44. 44. 44

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