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A forward search algorithm for detecting extreme study effects in network meta-analysis

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Listed:
  • Petropoulou, Maria
  • Salanti, Georgia
  • Rücker, Gerta
  • Schwarzer, Guido
  • Moustaki, Irini
  • Mavridis, Dimitris
Abstract
In a quantitative synthesis of studies via meta-analysis, it is possible that some studies provide a markedly different relative treatment effect or have a large impact on the summary estimate and/or heterogeneity. Extreme study effects (outliers) can be detected visually with forest/funnel plots and by using statistical outlying detection methods. A forward search (FS) algorithm is a common outlying diagnostic tool recently extended to meta-analysis. FS starts by fitting the assumed model to a subset of the data which is gradually incremented by adding the remaining studies according to their closeness to the postulated data-generating model. At each step of the algorithm, parameter estimates, measures of fit (residuals, likelihood contributions), and test statistics are being monitored and their sharp changes are used as an indication for outliers. In this article, we extend the FS algorithm to network meta-analysis (NMA). In NMA, visualization of outliers is more challenging due to the multivariate nature of the data and the fact that studies contribute both directly and indirectly to the network estimates. Outliers are expected to contribute not only to heterogeneity but also to inconsistency, compromising the NMA results. The FS algorithm was applied to real and artificial networks of interventions that include outliers. We developed an R package (NMAoutlier) to allow replication and dissemination of the proposed method. We conclude that the FS algorithm is a visual diagnostic tool that helps to identify studies that are a potential source of heterogeneity and inconsistency.

Suggested Citation

  • Petropoulou, Maria & Salanti, Georgia & Rücker, Gerta & Schwarzer, Guido & Moustaki, Irini & Mavridis, Dimitris, 2021. "A forward search algorithm for detecting extreme study effects in network meta-analysis," LSE Research Online Documents on Economics 110954, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:110954
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    More about this item

    Keywords

    forward search; Cook’s distance; NMAoutlier; outliers; network meta-analysis;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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