IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/v7dtq.html
   My bibliography  Save this paper

Methods to address confounding and other biases in meta-analyses: Review and recommendations

Author

Listed:
  • Mathur, Maya B
  • VanderWeele, Tyler

Abstract

Meta-analyses contribute critically to cumulative science, but they can produce misleading conclusions if their constituent primary studies are biased, for example by unmeasured confounding in nonrandomized studies. We provide practical guidance on how meta-analysts can address confounding and other biases that affect studies' internal validity, focusing primarily on sensitivity analyses that help quantify how biased the meta-analysis estimates might be. We review a number of sensitivity analysis methods to do so, especially recent developments that are straightforward to implement and interpret and that use somewhat less stringent statistical assumptions than earlier methods. We give recommendations for how these methods could be applied in practice and illustrate using a previously published meta-analysis. Sensitivity analyses can provide informative quantitative summaries of evidence strength, and we suggest reporting them routinely in meta-analyses of potentially biased studies. This recommendation in no way diminishes the importance of defining study eligibility criteria that reduce bias and of characterizing studies’ risks of bias qualitatively.

Suggested Citation

  • Mathur, Maya B & VanderWeele, Tyler, 2021. "Methods to address confounding and other biases in meta-analyses: Review and recommendations," OSF Preprints v7dtq, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:v7dtq
    DOI: 10.31219/osf.io/v7dtq
    as

    Download full text from publisher

    File URL: https://osf.io/download/61040319b2d3320086ffb43c/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/v7dtq?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Mathur, Maya B & VanderWeele, Tyler, 2020. "Robust metrics and sensitivity analyses for meta-analyses of heterogeneous effects," OSF Preprints r2s78, Center for Open Science.
    2. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    3. Shadish, William R. & Clark, M. H. & Steiner, Peter M., 2008. "Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1334-1344.
    4. Su Golder & Yoon K Loke & Martin Bland, 2011. "Meta-analyses of Adverse Effects Data Derived from Randomised Controlled Trials as Compared to Observational Studies: Methodological Overview," PLOS Medicine, Public Library of Science, vol. 8(5), pages 1-13, May.
    5. Maya B. Mathur & Tyler J. VanderWeele, 2020. "Sensitivity Analysis for Unmeasured Confounding in Meta-Analyses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 163-172, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Valérie Seegers & Ludovic Trinquart & Isabelle Boutron & Philippe Ravaud, 2013. "Comparison of Treatment Effect Estimates for Pharmacological Randomized Controlled Trials Enrolling Older Adults Only and Those including Adults: A Meta-Epidemiological Study," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-5, May.
    2. Whitney S Beck & Ed K Hall, 2018. "Confounding factors in algal phosphorus limitation experiments," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    3. Xindong Xue & W. Robert Reed & Robbie C.M. van Aert, 2022. "Social Capital and Economic Growth: A Meta-Analysis," Working Papers in Economics 22/20, University of Canterbury, Department of Economics and Finance.
    4. Bart Verkuil & Serpil Atasayi & Marc L Molendijk, 2015. "Workplace Bullying and Mental Health: A Meta-Analysis on Cross-Sectional and Longitudinal Data," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-16, August.
    5. Francesca Pilotto & Ingolf Kühn & Rita Adrian & Renate Alber & Audrey Alignier & Christopher Andrews & Jaana Bäck & Luc Barbaro & Deborah Beaumont & Natalie Beenaerts & Sue Benham & David S. Boukal & , 2020. "Meta-analysis of multidecadal biodiversity trends in Europe," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    6. repec:cup:judgdm:v:15:y:2020:i:6:p:972-988 is not listed on IDEAS
    7. Jonas Schmidt & Tammo H. A. Bijmolt, 2020. "Accurately measuring willingness to pay for consumer goods: a meta-analysis of the hypothetical bias," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 499-518, May.
    8. Mario Herberz & Tobias Brosch & Ulf J. J. Hahnel, 2020. "Kilo what? Default units increase value sensitivity in joint evaluations of energy efficiency," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(6), pages 972-988, November.
    9. Piers Steel & Sjoerd Beugelsdijk & Herman Aguinis, 2021. "The anatomy of an award-winning meta-analysis: Recommendations for authors, reviewers, and readers of meta-analytic reviews," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 52(1), pages 23-44, February.
    10. Augusteijn, Hilde Elisabeth Maria & van Aert, Robbie Cornelis Maria & van Assen, Marcel A. L. M., 2021. "Posterior Probabilities of Effect Sizes and Heterogeneity in Meta-Analysis: An Intuitive Approach of Dealing with Publication Bias," OSF Preprints avkgj, Center for Open Science.
    11. Heissel, Jennifer, 2016. "The relative benefits of live versus online delivery: Evidence from virtual algebra I in North Carolina," Economics of Education Review, Elsevier, vol. 53(C), pages 99-115.
    12. Georgiou, George K. & Guo, Kan & Naveenkumar, Nithya & Vieira, Ana Paula Alves & Das, J.P., 2020. "PASS theory of intelligence and academic achievement: A meta-analytic review," Intelligence, Elsevier, vol. 79(C).
    13. Geller, Susann & Wilhelm, Oliver & Wacker, Jan & Hamm, Alfons & Hildebrandt, Andrea, 2017. "Associations of the COMT Val158Met polymorphism with working memory and intelligence – A review and meta-analysis," Intelligence, Elsevier, vol. 65(C), pages 75-92.
    14. Gignac, Gilles E. & Bates, Timothy C., 2017. "Brain volume and intelligence: The moderating role of intelligence measurement quality," Intelligence, Elsevier, vol. 64(C), pages 18-29.
    15. Stephan Kambach & Ingolf Kühn & Bastien Castagneyrol & Helge Bruelheide, 2016. "The Impact of Tree Diversity on Different Aspects of Insect Herbivory along a Global Temperature Gradient - A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-14, November.
    16. Julian Packheiser & Helena Hartmann & Kelly Fredriksen & Valeria Gazzola & Christian Keysers & Frédéric Michon, 2024. "A systematic review and multivariate meta-analysis of the physical and mental health benefits of touch interventions," Nature Human Behaviour, Nature, vol. 8(6), pages 1088-1107, June.
    17. Katherine Baicker & Theodore Svoronos, 2019. "Testing the Validity of the Single Interrupted Time Series Design," NBER Working Papers 26080, National Bureau of Economic Research, Inc.
    18. Nan Wang & Yuxiang Luan & Rui Ma, 2024. "Detecting causal relationships between work motivation and job performance: a meta-analytic review of cross-lagged studies," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    19. repec:cup:judgdm:v:14:y:2019:i:3:p:234-279 is not listed on IDEAS
    20. Senlin Zhou & Yunpeng Wu & Xizheng Xu, 2023. "Linking Cognitive Reappraisal and Expressive Suppression to Mindfulness: A Three-Level Meta-Analysis," IJERPH, MDPI, vol. 20(2), pages 1-16, January.
    21. Mahesh Shumsher Rughooputh & Rui Zeng & Ying Yao, 2015. "Protein Diet Restriction Slows Chronic Kidney Disease Progression in Non-Diabetic and in Type 1 Diabetic Patients, but Not in Type 2 Diabetic Patients: A Meta-Analysis of Randomized Controlled Trials ," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-17, December.
    22. de la Cruz, Vera Ysabel V. & Tantriani, & Cheng, Weiguo & Tawaraya, Keitaro, 2023. "Yield gap between organic and conventional farming systems across climate types and sub-types: A meta-analysis," Agricultural Systems, Elsevier, vol. 211(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:v7dtq. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.