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Datos y análisis

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- 1. Systematic Review and Meta Analysis Abebe Minda (PhD)
- 2. Introduction • Definitions – Systematic review: is a systematic search for all available studies to answer a particular question – A systematic review seeks to identify, collate, and systematically summarize all empirical evidence on a specific research topic, using explicit, systematic, transparent, and replicable methods that are designed to minimize bias. • Provides a comprehensive summary of best evidence on a given research topic
- 3. Why do we need to do systematic review • There are limitless published studies – On which study do you want to rely? • Unmanageable information from individual study: – Biased – Methodologically flawed – Time and context dependent – Misinterpreted – Vary in conclusion • Need for comprehensive synthesis of existing knowledge
- 4. • Such synthesis leads to – Evidence based guidelines and policies – Separate sound from unsound evidence – Efficient use of resource – Reflection on need of further research
- 5. • We benefited from SR – Time – Cost – Right decision on what works • recognizes that consistent results across multiple primary studies provide more convincing evidence than results from any single primary study. – Future paths of investigation – Science to grow
- 6. • Goals of systematic review: – Appraise quality of existing studies – Provide high quality summary of the underlying evidence
- 7. • Users – Researchers • Current state of the art • Gap in evidence • Formulate further research – Practitioners • Inform best evidence based practice • Develop guidelines – Policy makers • Formulate best evidence based policy – Patients • Make best informed choice from alternatives
- 8. • Before formulating the question, establish need for a systematic review on the topic
- 9. Is there already an existing systematic review on the topic? No Proceed with one if sufficient evidence has emerged on the topic Yes No need No A new review is warranted Yes Yes No need Is existing review sufficiently comprehensive and exhaustive of the topic in terms of databases searched, any language restrictions or other restrictions? Is existing review still current or up-to-date since it was undertaken? Is existing review methodologically sound and of high quality? Have the various steps of conducting a review been adhered to or are some key steps missing? Yes No need No A new review is warranted No A new review is warranted
- 10. Steps in conducting a systematic review – Question – Develop protocol – Identify eligible studies – Appraise study quality – Extract data – Synthesize evidence – Interpret findings – Write up
- 11. • Four broad types of research questions are particularly appropriate for systematic reviews: – Questions about the etiology or epidemiology of particular conditions; – Questions about the efficacy or effectiveness of interventions or treatments addressing those conditions; and – Questions about group differences, either between naturally occurring groups (i.e., males and females) or between groups defined by researchers (e.g., between different diagnostic groups). – Other types of systematic review questions might focus on • prevalence of conditions • changes in conditions over time • diagnostic test accuracy • questions related to economic evidence
- 12. Formulation of research questions • The PICOS/PECO/PEO framework – P = Participant – I/E = Intervention or Exposure – C = Comparator – O = Outcome – S = Study designs • Good guide to components of well built SR question • Enhances identification of relevant studies
- 13. • Participants – In terms of disease/condition – Important characteristics/demographics • Age • Sex • Ethnicity – Setting • Facility • Community • Developed/Low and middle income countries
- 14. • Intervention/Exposure – Relevant mostly for intervention and etiological studies • Dose, duration, timing, route, etc.
- 15. • Comparison – The alternative being compared to the intervention – Should be specific and limited to one or two alternatives
- 16. • Outcome – Dichotomous or continuous – Mortality, morbidity, quality of life, effectiveness, efficiency, efficacy, etc. • Example: – ‘‘Is [intervention X1] more effective than [intervention X2] in addressing [Y outcomes] in [Z population or context]?’’
- 17. • Study design – RCTs – Cohort – Case control – Cross sectional – Correlational – Case series – Case report
- 20. Features of a Good SR question • Relevance: – Policy – Practice – Care – Research • Feasibility • Ethically sound • Novelty • Narrowness/broadness
- 21. Exercise – Effect of cigarette smoking on low birth weight
- 22. Developing a Protocol • After you identified your area of interest – Refine a topic – Articulate central questions or hypotheses – Affix the scope of a review • Develop a specific inclusion and exclusion criteria – to clarify central concepts and set the boundaries of a review.
- 23. Identifying eligible studies • Get information from the papers Data bases Grey Literature • The JBI Library • The Cochran Library • Medline • Embase • CINAHL • Mednar • WorldWideScience • PsycEXTRA • Google Scholar
- 24. SEARCH STRATEGY • Searching relevant literature – Identify key words from the identified research topic – Use of Boolean operators • And • Or • Not
- 25. Additional sources • Reference list of retrieved articles • Manual searching of relevant publications • Experts in the field • Correspondence with first authors of published studies identified for the systematic review.
- 26. Search Logistics Search Strategy Search Sources Export citations to bibliographic software • Apply Search Strategy to databases • Export to bibliographic software – E.g. Endnote • Document the process
- 27. Select Studies • Selection of studies is an initial assessment • It addresses the question “should the paper be retrieved?” • Aims to select only those studies that address the review question – Match with the inclusion criteria • Scan titles and abstracts • If uncertain? - Retrieve - scan full text • The selection should be: – Transparent – Reproducible
- 28. Inclusion/exclusion criteria • Reviewers must determine which studies are relevant and suitable for inclusion in the systematic review. • The criteria for study selection should be determined ahead of time to the extent possible, – Reduce selection • The specific inclusion criteria will depend on the study question – PICOS
- 29. • Studies will be included, if they fulfill the following eligibility criteria: – Study designs: • Observational analytic – Study settings: • All countries – Participants: • Men, women & children – Exposures: – Outcome measures: – Publication Status: • All published data, abstracts and grey literature – Timings: • All dates – Language of Published Articles: • English language
- 30. Exclusion Criteria • Keep log of excluded studies • Note reasons for exclusion • Have eligibility checked by more than one reviewer • Develop strategy to resolve disagreements
- 31. Appraise study quality QUORUM for trails Moher et al. Randomized controlled trials: The QUORUM statement. Lancet 1999; 354:1896-1900 MOOSE for observational designs Meta-analysis of observational studies in epidemiology. JAMA 2000;283:2008-12 Newcastle-Ottawa quality assessment scale (NOS) A quality score was calculated based on three major components: 1. selection of the groups of study 2. comparability 3. ascertainment of the exposure and outcome.
- 32. NOS score • 7 or more ( a maximum of 7 for cross-sectional studies) • 9 for case-control and Cohort studies chosen to indicate a high standard for comparative observational studies. Review with the participants (NOS tool) • JBI quality appraisal tools for all study design
- 33. 521 references 317 references Scanned Ti/Ab 204 duplicates 38 studies retrieved 279 do not meet Inclusion criteria 11 do not meet Inclusion criteria 27 studies for Critical Appraisal
- 34. Extract data • Design and pilot data abstraction form – Consider more than one reviewers
- 35. Data abstraction elements • Publication details • Study design • Population details (n, characteristics) • Intervention details • Setting • Outcomes and findings Sample Data abstraction format is available http://www.cochrane.org
- 36. Sample data abstraction form Author & year of publication Country Objective of the study Study design Participants Main findings
- 37. • May require manipulating data into common format – Calculating estimates not given in study e.g. CI, SE – Contact authors to request info not given in the study • Compare extraction between reviewers
- 39. Synthesize evidence • Collation and summary of findings to drive reliable conclusion • Consider the strengths of overall evidence – Consistency of results – Possible reasons for any inconsistencies • Can be: – Narrative-only synthesis • If we can’t combine the body of evidence statistically – Quantitative summary-Mata analysis • If we can combine the body of evidence statistically
- 40. Qualitative systematic review (best evidence synthesis) The results of primary studies are summarized Not statistically combined Described narratively Still use other methods to limit bias Quantitative systematic review (meta-analysis) The results of two or more primary studies are combined Statistically combined • Individual patient data (IPD)/Pooled analysis • Aggregate patient data (APD) Use methods to limit bias
- 42. Write up
- 43. How do we decide whether to MA? • It depends on: – Heterogeneity • Clinical • Methodological – Homogeneity • Clinical • Methodological
- 44. Meta analysis • Meta analysis is a statistical analysis that combines the results of multiple scientific studies judged to be combinable. • Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error. • The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. • Meta-analytic results are considered the most trustworthy source of evidence to evidence based practice.
- 45. Purpose of Meta analysis • Uses statistics to summarize results of independent studies – Provide more precise estimates of the effects of health care studies than the individual studies – Facilitates investigation of the consistency and differences across studies
- 46. • The goal of a synthesis is to understand the results of any study in the context of all other studies. • First, we need to know whether or not the effect size is consistent across the body of data. • The effect size is a value which reflects the magnitude of the treatment effect or the strength of a relationship between two variables.
- 47. • It is the unit of currency in a meta-analysis. • It is used as the dependent (outcome) variable in a meta-analysis. • We compute the effect size for each study, and then work with the effect sizes to assess the consistency of the effect across studies and to compute a summary effect.
- 48. Cont…
- 49. • The effect size could represent the impact of an intervention, such as – the impact of medical treatment on risk of infection, – the impact of a teaching method on test scores, • The effect size is not limited to the impact of interventions, but could represent any relationship between two variables, such as – the difference in test scores for males versus females, – the difference in cancer rates for persons exposed or not exposed to second-hand smoke, or – the difference in cardiac events for persons with two distinct personality types. • In fact, what we generally call an effect size could refer simply to the estimate of a single value.
- 50. • Common Effect size metrics: – Odds ratio – Relative risk ratio – Risk difference – Correlation coefficient – Hazard ratio – Proportion – Measure of central tendency (mean, median) – Unstandardized mean difference – Standardized mean difference
- 51. How to choose an effect size • There are three major considerations: – The effect sizes from the different studies should be comparable to one another in the sense that they measure (at least approximately) the same thing – Estimates of the effect size should be computable from the information that is likely to be reported in published research reports. – The effect size should have good technical properties • Sampling distribution should be known so that variances and confidence intervals can be computed
- 52. Fixed Effect and Random Effect Models • Most meta-analyses are based on one of two statistical models, – Fixed-effect model or – Random-effects model
- 54. Fixed Effect Model • Under the fixed-effect model we assume that: – All studies in the meta-analysis share a common (true) effect size. OR – All factors that could influence the effect size are the same in all the studies, and therefore the true effect size is the same (hence the label fixed) in all the studies. • Since all studies share the same true effect, it follows that the observed effect size varies from one study to the next only because of the random error inherent in each study.
- 55. PERFORMING A FIXED-EFFECT META- ANALYSIS • In an actual meta-analysis we start with the observed effects and trying to estimate the population effect. • In order to obtain the most precise estimate of the population effect (to minimize the variance) we compute a weighted mean, where the weight assigned to each study is the inverse of that study’s variance.
- 56. • Concretely, the weight assigned to each study in a fixed-effect meta-analysis is Wi = 1 𝑉𝑌𝑖 , where, VYi is the within-study variance for study (i) • The weighted mean (M) is then computed as • that is, the sum of the products WiYi (weight multiplied by effect size) divided by the sum of the weights
- 57. • The variance of the summary effect is estimated as the reciprocal of the sum of the weights, or • And the estimated standard error of the summary effect is then the square root of the variance,
- 58. • Then, 95% lower and upper limits for the summary effect are estimated as: LLM = M – 1.96 * SEM and ULM = M + 1.96 * SEM • Finally, a Z-value to test the null hypothesis that the common true effect is zero can be computed using
- 59. Random Effects Model • Under the random-effects model we allow that the true effect could vary from study to study (randomly distributed). • For example, the effect size might be higher (or lower) in studies where the participants are older, or more educated, or healthier than in others, or when a more intensive variant of an intervention is used, and so on.
- 60. PERFORMING A RANDOM-EFFECTS META-ANALYSIS • Our goal is to use the collection of Yi to estimate the overall mean, μ. • In order to obtain the most precise estimate of the overall mean (to minimize the variance) we compute a weighted mean, where the weight assigned to each study is the inverse of that study’s variance.
- 61. • More generally, the observed effect Yi for any study is given by the grand mean, the deviation of the study’s true effect from the grand mean, and the deviation of the study’s observed effect from the study’s true effect. • To compute a study’s variance under the random- effects model, we need to know both the within- study variance and T2 , since the study’s total variance is the sum of these two values.
- 62. • The parameter T2 (tau-squared) is the between-studies variance (the variance of the effect size parameters across the population of studies). • In other words, if we somehow knew the true effect size for each study, and computed the variance of these effect sizes (across an infinite number of studies), this variance would be T2.
- 63. • T2 is estimated by using the moments (or the DerSimonian and Laird) method, as follows: Where, and Q statistic (a measure of weighted squared deviations)
- 64. Estimating the mean effect size • Under the random-effects model the weight assigned to each study is • where VYi* is the within-study variance for study i plus the between-studies variance, T2 . That is,
- 65. • The weighted mean, M*, is then computed as • That is, the sum of the products (weight multiplied by effect size) divided by the sum of the weights.
- 66. • The variance of the summary effect is estimated as the reciprocal of the sum of the weights, or • and the estimated standard error of the summary effect is then the square root of the variance,
- 67. • The 95% lower and upper limits for the summary effect would be computed as LLM* = M* - 1:96 x SEM* and ULM* = M* + 1:96 x SEM* • Finally, a Z-value to test the null hypothesis that the mean effect μ is zero could be computed using
- 68. WHICH MODEL SHOULD WE USE? • Fixed effect • We use the fixed-effect model if two conditions are met – First, we believe that all the studies included in the analysis are functionally identical. – Second, our goal is to compute the common effect size for the identified population, and not to generalize to other populations.
- 69. • Random effects – If the goal of analysis is usually to generalize to a range of scenarios. – If the data is gathered from the published literature, the random effects model is generally a more plausible match
- 70. Tests for heterogeneity • The usual way of assessing whether there is true heterogeneity in a meta - analysis is the Q-test and I-square • The classical measure of heterogeneity is Cochran’s Q, which is calculated as the weighted sum of squared differences between individual study effects and the pooled effect across studies, with the weights being those used in the pooling method. • The classical measure of heterogeneity is Cochran’s Q, which is calculated as the weighted sum of squared differences between individual study effects and the pooled effect across studies, with the weights being those used in the pooling method.
- 71. • I-square …… very low-------fixed model – The I² statistic describes the percentage of variation across studies that is due to heterogeneity rather than chance. – I² = 100% x (Q-df)/Q – I² is an intuitive and simple expression of the inconsistency of studies’ results. • The purpose of this test was to assess the extent of variation between the sample estimates.
- 72. Tests for publication bias • Egger’s test is commonly used to assess potential publication bias in a meta-analysis via funnel plot asymmetry • It is test is a linear regression of the intervention effect estimates on their standard errors weighted by their inverse variance. • A funnel plot is a scatter plot of treatment effect against a measure of study precision. • It is used primarily as a visual aid for detecting bias or systematic heterogeneity. • A symmetric inverted funnel shape arises from a ‘well-behaved’ data set, in which publication bias is unlikely.