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http://dx.doi.org/10.5351/KJAS.2020.33.4.439

Pairwise pseudolikelihood approach for adjusting selection bias in meta-analysis  

Kuk, Sunghee (Department of Statistics, Inha University)
Lee, Woojoo (Department of Public Health Science, Graduate School of Public Health Seoul National University)
Publication Information
The Korean Journal of Applied Statistics / v.33, no.4, 2020 , pp. 439-449 More about this Journal
Abstract
Meta-analysis provides a way of integrating several independent studies of interest. Since small studies with statistically significant results are more likely to be published, publication bias, which is a special case of selection bias, often occurs in meta analysis. Conditional likelihood and weighted estimating equation have been proposed to deal with publication bias, but they require to specify a correct selection probability model. In contrast, the pairwise pseudolikelihood approach can correct publication bias without fully specifying the correct selection probability model, but its performance in meta-analysis was not investigated. In this paper, we perform a numerical study about whether the pairwise pseudolikelihood approach is effective for solving publication bias arising from typical meta-analysis settings.
Keywords
meta-analysis; pairwise pseudolikelihood; publication bias; selection bias;
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