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Robust confidence intervals for trend estimation in meta-analysis with publication bias

Author

Listed:
  • H. Lu
  • P. Yin
  • R.X. Yue
  • J.Q. Shi

Abstract

Confidence interval (CI) is very useful for trend estimation in meta-analysis. It provides a type of interval estimate of the regression slope as well as an indicator of the reliability of the estimate. Thus a precise calculation of confidence interval at an expected level is important. It is always difficult to explicitly quantify the CIs when there is publication bias in meta-analysis. Various CIs have been proposed, including the most widely used DerSimonian-Laird CI and the recently proposed Henmi-Copas CI. The latter provides a robust solution when there are non-ignorable missing data due to publication bias. In this paper we extended the idea into meta-analysis for trend estimation. We applied the method in different scenarios and showed that this type of CI is more robust than the others.

Suggested Citation

  • H. Lu & P. Yin & R.X. Yue & J.Q. Shi, 2015. "Robust confidence intervals for trend estimation in meta-analysis with publication bias," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2715-2733, December.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2715-2733
    DOI: 10.1080/02664763.2015.1048672
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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. John Copas & Dan Jackson, 2004. "A Bound for Publication Bias Based on the Fraction of Unpublished Studies," Biometrics, The International Biometric Society, vol. 60(1), pages 146-153, March.
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