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Quantile regression in random effects meta-analysis model

Author

Listed:
  • Xiaowen Dai

    (Shanghai Lixin University of Accounting and Finance
    Shanghai Lixin University of Accounting and Finance)

  • Libin Jin

    (Shanghai Lixin University of Accounting and Finance
    Shanghai Lixin University of Accounting and Finance)

  • Lei Shi

    (Yunnan University of Finance and Economics)

Abstract

In meta-analysis model, due to the appearance of publication bias or outliers, as well as the small sample size, the normal assumption is usually unreliable. Therefore, the exploration of more robust estimation, such quantile regression (QR) method, is extremely important in meta-analysis area. This paper studies the QR estimation method in random-effects meta-analysis model based on the reformulation by asymmetric Laplace distribution (ALD). The maximum likelihood estimation using Monte Carlo Expectation Maximization algorithm and the Bayesian estimation using Markov chain Monte Carlo (MCMC) algorithm are proposed for computation of the QR estimates. The significance tests of regression coefficients are suggested using likelihood ratio statistics. For MCMC algorithm, a simple and efficient Gibbs sampling algorithm is employed based on a location-scale mixture representation of the ALD, and information criterions are considered for choosing the hyper-parameters. Monte Carlo simulations are conducted to study the finite sample performance of the proposed methodology and analysis of two real data sets are presented for illustrations. Our results show that QR estimation methods perform very well, especially in case of non-normal assumption in meta-regression models. The detailed algorithms and software code are available for easy use in applications.

Suggested Citation

  • Xiaowen Dai & Libin Jin & Lei Shi, 2023. "Quantile regression in random effects meta-analysis model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 469-492, June.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:2:d:10.1007_s10260-022-00660-3
    DOI: 10.1007/s10260-022-00660-3
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    References listed on IDEAS

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