Presentation on theme: "Practical Meta-Analysis"— Presentation transcript:
1Practical Meta-Analysis These lectures are based on the bookPractical Meta-Analysis by David B. Wilson & Mark W. Lipsey
2Outline of Meta-Analysis Topics covered will include:What is meta-analysis ?Meta-analysis issues:Problem definition and basic conceptsFinding eligible studies, criterion for inclusionCoding issues, key variables, etc.Effect sizes (ES) and computational issuesCombining effect sizes using meta-analysisPublication biasInterpretation of resultsEvaluating the quality of a meta-analysis
3Outline of Meta-Analysis Topics covered will include:Effect Sizes (ES) – these are combined using meta-analysisConducting a meta-analysis – how do we combine effect sizes from multiple studies to obtain an aggregate effect size.
4What is Meta-analysis?Meta-analysis focuses on the aggregation the findings of from multiple research studies.These studies must produce quantitative findings from empirical research.In order combine the study results we need a standard measure of effect size (ES) that can be calculated for each study and then combined.
5What is Meta-analysis?Meta-analysis essentially takes a weighted average of the effect sizes from each of the studies.The weights take into account sample sizes and variability of the results from each study.We also may need to account for random differences between studies due to variations in procedures, settings, populations sampled, etc.
6Example: Effectiveness of Correctional Boot-Camps for Prisoners 32 unique studies were found, that reported on 43 independent boot-camp/comparison samples looking recidivism as the response.For each study, the odds ratio for recidivism was calculated looking at the “benefit” of the prisoner boot-camps.Random differences between the differences in the study designs, prisoner populations, protocols, etc. were accounted for.
7Forest Plot from a Meta-Analysis oft Correctional Boot-Camps (Wilson et al.)
8The Great Debate & Historical Background 1952: Hans J. Eysenck concluded that there were no favorable effects of psychotherapy, starting a raging debate.20 years of evaluation research and hundreds of studies failed to resolve the debate1978: To prove Eysenck wrong, Gene V. Glass statistically aggregated the findings of 375 psychotherapy outcome studies.Glass (and colleague Smith) concluded that psychotherapy did indeed workGlass called his method “meta-analysis”
9The Emergence of Meta-Analysis The ideas behind meta-analysis predate Glass’ work by several decadesKarl Pearson (1904)averaged correlations for studies of the effectiveness of inoculation for typhoid feverR. A. Fisher (1944)“When a number of quite independent tests of significance have been made, it sometimes happens that although few or none can be claimed individually as significant, yet the aggregate gives an impression that the probabilities are on the whole lower than would often have been obtained by chance”.Source of the idea of cumulating probability values
10The Emergence of Meta-analysis Ideas behind meta-analysis predate Glass’ work by several decades.W. G. Cochran (1953)Discusses a method of averaging means across independent studiesLaid-out much of the statistical foundation that modern meta-analysis is built upon (e.g., Inverse variance weighting and homogeneity testing)
11The Logic of Meta-analysis Traditional methods of review focus on statistical significance testingSignificance testing is not well suited to this taskHighly dependent on sample sizeNull finding does not carry the same “weight” as a significant findingsignificant effect is a strong conclusionnon-significant effect is a weak conclusionMeta-analysis focuses on the direction and magnitude of the effects across studies, not statistical significanceIsn’t this what we are interested in anyway?Direction and magnitude are represented by the effect size
12When Can You Do Meta-Analysis? Meta-analysis is applicable to collections of research thatAre empirical, rather than theoreticalProduce quantitative results, rather than qualitative findingsExamine the same constructs and relationshipsHave findings that can be configured in a comparable statistical form (e.g., as effect sizes, correlation coefficients, odds-ratios, proportions)Are “comparable” given the question at hand
13Forms of Research Findings Suitable to Meta-analysis Central tendency researchPrevalence ratesPre-post contrastsGrowth ratesGroup contrastsExperimentally created groupsComparison of outcomes between treatment and comparison groupsNaturally occurring groupsComparison of spatial abilities between boys and girlsRates of morbidity among high and low risk groups
14Forms of Research Findings Suitable to Meta-analysis Association between variablesMeasurement researchE.g. Validity generalizationIndividual differences researchE.g. Correlation between personality constructs
15Effect Size: The Key to Meta-Analysis The effect size (ES) makes meta-analysis possibleIt is the “dependent variable” in a meta-analysisIt standardizes findings across studies such that they can be directly compared and aggregated.
16Effect Size: The Key to Meta-Analysis Any standardized index can be an “effect size” (e.g., standardized mean difference, correlation coefficient, odds-ratio) as long as it meets the following:It is comparable across studies (generally requires standardization)Represents the magnitude and direction of the relationship of interestIt is independent of sample sizeDifferent meta-analyses may use different effect size indices.
17The Replication Continuum PureReplicationsConceptualReplicationsYou must be able to argue that the collection of studies you are meta-analyzing examine the same relationship. This may be at a broad level of abstraction, such as the relationship between a risk factor and disease incidence or between a medical therapy and outcome of interest. Alternatively it may be at a narrow level of abstraction and represent pure replications.The closer to pure replications your collection of studies, the easier it is to argue comparability.
18Which Studies to Include? It is critical to have an explicit inclusion and exclusion criteria. The broader the research domain, the more detailed they tend to become.May need to refine criteria as you interact with the literature.Components of a detailed criteria for inclusion:distinguishing featuresresearch respondentskey variablesresearch methodscultural and linguistic rangetime framepublication types
19Methodological Quality Dilemma Include or exclude low quality studies?The findings of all studies are potentially in error (methodological quality is a continuum, not a dichotomy, i.e. bad vs. good)Being too restrictive may restrict ability to generalizeBeing too inclusive may weaken the confidence that can be placed in the findings.Methodological quality is often in the “eye-of-the-beholder”.You must strike a balance that is appropriate to your research question.
20Searching Far and WideThe “we only included published studies because they have been peer-reviewed” argument.Significant findings are more likely to be published than non-significant findings. This is a major source of bias!Critical to try to identify and retrieve all studies that meet your eligibility criteria
21Searching Far and WidePotential sources for identification of documents:Computerized bibliographic databases (WSU Databases – PubMed, Proquest, OVID, CINAHL, etc.)“Google” internet search engineAuthors working in the research domain ( a relevant Listserv?)Conference programs and proceedingsDissertationsReview articlesHand searching relevant journalsGovernment reports, bibliographies, clearinghouses
22A Note About Computerized Bibliographies Rapidly changing areaGet to know your local librarian!Searching one or two databases is generally inadequate.Use “wild cards” (e.g., random? will find random, randomization, and randomize)Throw a wide net; filter down with a manual reading of the abstracts
23Strengths of Meta-Analysis Imposes a discipline on the process of summing up research findingsRepresents findings in a more differentiated and sophisticated manner than conventional reviewsCapable of finding relationships across studies that are obscured in other approachesProtects against over-interpreting differences across studiesCan handle a large numbers of studies (this would overwhelm traditional approaches to review)
24Weaknesses of Meta-Analysis Requires a good deal of effortMechanical aspects don’t lend themselves to capturing more qualitative distinctions between studies“Apples and oranges” criticismMost meta-analyses include “blemished” studies to one degree or another (e.g., a randomized design with attrition)
25Weaknesses of Meta-Analysis Selection bias poses a continual threatNegative and null finding studies that you were unable to find.Outcomes for which there were negative or null findings that were not reported.