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Bias modelling in evidence synthesis

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  • Rebecca M. Turner
  • David J. Spiegelhalter
  • Gordon C. S. Smith
  • Simon G. Thompson

Abstract

Summary. Policy decisions often require synthesis of evidence from multiple sources, and the source studies typically vary in rigour and in relevance to the target question. We present simple methods of allowing for differences in rigour (or lack of internal bias) and relevance (or lack of external bias) in evidence synthesis. The methods are developed in the context of reanalysing a UK National Institute for Clinical Excellence technology appraisal in antenatal care, which includes eight comparative studies. Many were historically controlled, only one was a randomized trial and doses, populations and outcomes varied between studies and differed from the target UK setting. Using elicited opinion, we construct prior distributions to represent the biases in each study and perform a bias‐adjusted meta‐analysis. Adjustment had the effect of shifting the combined estimate away from the null by approximately 10%, and the variance of the combined estimate was almost tripled. Our generic bias modelling approach allows decisions to be based on all available evidence, with less rigorous or less relevant studies downweighted by using computationally simple methods.

Suggested Citation

  • Rebecca M. Turner & David J. Spiegelhalter & Gordon C. S. Smith & Simon G. Thompson, 2009. "Bias modelling in evidence synthesis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 21-47, January.
    • Handle: RePEc:bla:jorssa:v:172:y:2009:i:1:p:21-47
    DOI: 10.1111/j.1467-985X.2008.00547.x
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    References listed on IDEAS

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    1. Sander Greenland, 2005. "Multiple‐bias modelling for analysis of observational data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 267-306, March.
    2. Jeremy E. Oakley & Anthony O'Hagan, 2007. "Uncertainty in prior elicitations: a nonparametric approach," Biometrika, Biometrika Trust, vol. 94(2), pages 427-441.
    3. A. E. Ades & A. J. Sutton, 2006. "Multiparameter evidence synthesis in epidemiology and medical decision‐making: current approaches," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 5-35, January.
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    2. Danila Azzolina & Paola Berchialla & Dario Gregori & Ileana Baldi, 2021. "Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
    3. K. M. Rhodes & J. Savović & R. Elbers & H. E. Jones & J. P. T. Higgins & J. A. C. Sterne & N. J. Welton & R. M. Turner, 2020. "Adjusting trial results for biases in meta‐analysis: combining data‐based evidence on bias with detailed trial assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 193-209, January.
    4. van der Bles, Anne Marthe & van der Liden, Sander & Freeman, Alessandra L. J. & Mitchell, James & Galvao, Ana Beatriz & Spiegelhalter, David J., 2019. "Communicating uncertainty about facts, numbers, and science," EMF Research Papers 22, Economic Modelling and Forecasting Group.
    5. Isabelle Albert & Emmanuelle Espié & Henriette de Valk & Jean‐Baptiste Denis, 2011. "A Bayesian Evidence Synthesis for Estimating Campylobacteriosis Prevalence," Risk Analysis, John Wiley & Sons, vol. 31(7), pages 1141-1155, July.
    6. Karla Hemming & Peter J Chilton & Richard J Lilford & Anthony Avery & Aziz Sheikh, 2012. "Bayesian Cohort and Cross-Sectional Analyses of the PINCER Trial: A Pharmacist-Led Intervention to Reduce Medication Errors in Primary Care," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-8, June.
    7. Giancarlo Manzi & David J. Spiegelhalter & Rebecca M. Turner & Julian Flowers & Simon G. Thompson, 2011. "Modelling bias in combining small area prevalence estimates from multiple surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 31-50, January.
    8. John J. Graff & Nalini Sathiakumar & Maurizio Macaluso & George Maldonado & Robert Matthews & Elizabeth Delzell, 2009. "The Effect of Uncertainty in Exposure Estimation on the Exposure-Response Relation between 1,3-Butadiene and Leukemia," IJERPH, MDPI, vol. 6(9), pages 1-20, September.
    9. Giancarlo MANZI & Pier Alda FERRARI, "undated". "Statistical methods for evaluating satisfaction with public services Abstract: Contrary to private enterprises, public enterprises can be unaware of the impact of their performance when providing serv," CIRIEC Working Papers 1404, CIRIEC - Université de Liège.
    10. Geneletti, Sara & Mason, Alexina & Best, Nicky, 2011. "Adjusting for selection effects in epidemiologic studies: why sensitivity analysis is the only “solution”," LSE Research Online Documents on Economics 31520, London School of Economics and Political Science, LSE Library.
    11. Desiree C Wilks & Stephen J Sharp & Ulf Ekelund & Simon G Thompson & Adrian P Mander & Rebecca M Turner & Susan A Jebb & Anna Karin Lindroos, 2011. "Objectively Measured Physical Activity and Fat Mass in Children: A Bias-Adjusted Meta-Analysis of Prospective Studies," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-8, February.
    12. David M. Phillippo & Sofia Dias & A. E. Ades & Vanessa Didelez & Nicky J. Welton, 2018. "Sensitivity of treatment recommendations to bias in network meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 843-867, June.
    13. McCandless Lawrence C., 2012. "Meta-Analysis of Observational Studies with Unmeasured Confounders," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-31, January.
    14. Davis, Alexander L. & Krishnamurti, Tamar & Fischhoff, Baruch & Bruine de Bruin, Wandi, 2013. "Setting a standard for electricity pilot studies," Energy Policy, Elsevier, vol. 62(C), pages 401-409.
    15. Christopher H. Jackson & Linda D. Sharples & Simon G. Thompson, 2010. "Structural and parameter uncertainty in Bayesian cost‐effectiveness models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 233-253, March.
    16. C Elizabeth McCarron & Eleanor M Pullenayegum & Lehana Thabane & Ron Goeree & Jean-Eric Tarride, 2011. "Bayesian Hierarchical Models Combining Different Study Types and Adjusting for Covariate Imbalances: A Simulation Study to Assess Model Performance," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-7, October.
    17. S. Dias & N. J. Welton & V. C. C. Marinho & G. Salanti & J. P. T. Higgins & A. E. Ades, 2010. "Estimation and adjustment of bias in randomized evidence by using mixed treatment comparison meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(3), pages 613-629, July.
    18. Ian Wadsworth & Lisa V. Hampson & Thomas Jaki & Graeme J. Sills & Anthony G. Marson & Richard Appleton, 2020. "A quantitative framework to inform extrapolation decisions in children," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 515-534, February.
    19. Mathur, Maya B & VanderWeele, Tyler, 2018. "Statistical methods for evidence synthesis," Thesis Commons kd6ja, Center for Open Science.
    20. Rebecca M Turner & Myfanwy Lloyd-Jones & Dilly O C Anumba & Gordon C S Smith & David J Spiegelhalter & Hazel Squires & John W Stevens & Michael J Sweeting & Stanislaw J Urbaniak & Robert Webster & Sim, 2012. "Routine Antenatal Anti-D Prophylaxis in Women Who Are Rh(D) Negative: Meta-Analyses Adjusted for Differences in Study Design and Quality," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-10, February.

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