Which is appropriate to use fixed-effect or random effect statistical model while conducting meta-analyses - Pubrica

1. WHICHISAPPROPRIATETOUSE FIXED-EFFECTORRANDOMEFFECT STATISTICALMODELWHILE CONDUCTINGMETA-ANALYSES AnAcademicpresentationby Dr.NancyAgnes,Head,TechnicalOperations,Pubrica Group: www.pubrica.com Email:sales@pubrica.com

2. Today'sDiscussion Outline Introduction Meta-analyses-DifferentComponents FixedandRandomEffectModels TheComparison Conclusion

3. Introduction Meta-analysisis the statistical analysis of data andanessentialaspectofsystematicreviews. Here,thestudyisconductedbasedon mathematicalmodels. Meta-analysesareapplicableforanumberof purposes,includingsynthesizingdataonthe results of interventions and supporting evidence- basedpolicy andpractice. Contd...

4. A meta-analysis is a tool with multiple applications in various fields like medicine, healthcare,pharmaceuticals,psychology,ecology,education,criminologyand business(Borenstein 2021). The two most popular models used in conducting meta-analyses are fixed-effect and random-effectmodels. Thisreviewfocusesonandcomparesboththesemodelsbasedonrecentstudies.

5. Meta-analyses - DifferentComponents Ameta-analysisisastatisticalmethodfor combining quantitative data from various studies that address the same or similar research question (Schober2020). Anumberofstepsareinvolvedinconductinga meta-analysis. Contd...

6. These include - question framing, formation of search strategy, the search of literature database, selection of articles, data extraction, examination of quality of the articles, test for heterogeneity, estimation of summary effect, evaluation of the sources of heterogeneity,assessmentofpublicationbiasandfinally,presentationofresults (Wang2021). Fixed-effect and random-effect methods help in measuring the summary effect of a meta-analysis.Both theseapproaches are verydistinctive.

7. Fixed and Random EffectModels In the fixed-effect model, it is assumed that there is one real effect that underpins all of the studies in the research,andthatallvariationsinobservedresults areduetosamplingerrorinthefixed-effectmodel. Itisalsoknownasthecommon-effectmodel. In this model, all variables that could affect the effect size is the same across all studies, and therefore the trueeffectsizeisthesameacrossall studies(Borenstein2021). Contd...

8. Thepooledorsummary effectinafixed-effect meta-analysis estimates this typical true effect size (Schober2020). Two conditions must be satisfied in order for a fixed- effectmodel tobe applied. To begin, one must be confident in the similarity of all studies included in the meta-analysisand that synthesizingthedatais appropriate. Contd...

9. Next, calculation of common-effect size is considered that is only applicable to the meta-analysis population (Spineli2020a). Random effect models, on the other hand, presume a different underlying effect for each sample and treat thisasarandomsourceofvariance(Wang2021). In this model, It is often assumed that true effects are normally distributed, or they differ from study to study (Borenstein2021).

10. The Comparison Bothofthesewidelyusedmeta-analysismodelshavetheir ownset of limitations. Whentheheterogeneityof studiescannot beneglected, the common-effectmodelcanproducemisleadingresults. When the number of studies is small, the CI (confidence interval)forthemeaneffectbasedontherandom-effect modelcan betoo largeto behelpful. Contd...

11. Sincealargeportionofmeta-analysesincludes numbersofstudieswithnon-negligiblevariability, theselimitationsaresignificantroadblocksin practice(Lin 2020). Anotherpointtonoteis,aswecomparethe weighting schemes of these two models, we can observe that as we move from a fixed-effect to a random-effects model, larger studies tend to lose influence, and smaller studies tend to gain influence (Spineli2020 *(a)). Contd...

12. Furthermore,thedifferentassumptionsforfixed-effectandrandom-effectmodels indicate different meanings of the variance (resulting in distinguished computations of the meta-analysis results due to varying weighting schemes) and the distinguished null hypothesisof no linearcorrelation determinations. The only source of error in a fixed-effect model is within-study variance and, the null hypothesisstatesthatthetypicaltrueeffectsizeisunrelatedtothecovariateofinterest. Contrastinglyinarandom-effectsmodel,bothwithin-studyandbetween-study variances are sources of error and, the null hypothesis states that the mean of the true effectsize isunrelated tothe covariateof interest(Spineli 2020*(b)).

13. Conclusion Toconclude,afixed-effectmodelcanonlybeapplied iftherearepotentialfactorsthatindicatethatthe studiesinvolvedareidenticalforallintentsand purposes(Borenstein 2007). However, implementation of the fixed-effect model is rarelypossible inpractice. The effect size varies from study to study in real- worldsynthesis. Contd...

14. Contd...

15. It is due to a variety of factors like differences in participant mixes and intervention implementation. As a result, a random-effects model appears to be sufficient and efficient in most meta-analyses(Spineli 2020 *(a)). The question of which model matches the distribution of effect sizes and takes into accounttheappropriatesource(s)oferrormustbethesoleconsiderationwhen choosinga model (Borenstein 2021). Finally, the best model to use depends highly on the type of study conducted and the natureof the goalsthat it wants toachieve.

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