Asymptotic Analysis of the Forward Search

Johansen, Søren; Nielsen, Bent

doi:10.2139/ssrn.2221850

Cited by 2 publications

(1 citation statement)

References 27 publications

(20 reference statements)

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“…There is currently a lot of work in constructing these bands without simulation and we aim to equip the FS for NMA with this capability in the future. Johansen et al provided the asymptotic distribution of scaled forward residuals 52 offered in R package ForwardSearch. 53 Of course, the same problems apply also to the backward methods.…”

Section: Discussionmentioning

confidence: 99%

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

Πετροπούλου

Salanti

Rücker

et al. 2021

Statistics in Medicine

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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.

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Section: Discussionmentioning

confidence: 99%

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

Πετροπούλου

Salanti

Rücker

et al. 2021

Statistics in Medicine

View full text Add to dashboard Cite

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Outlier Detection in Regression Using an Iterated One-Step Approximation to the Huber-Skip Estimator

Johansen

Nielsen

2013

Econometrics

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In regression we can delete outliers based upon a preliminary estimator and re-estimate the parameters by least squares based upon the retained observations. We study the properties of an iteratively defined sequence of estimators based on this idea. We relate the sequence to the Huber-skip estimator. We provide a stochastic recursion equation for the estimation error in terms of a kernel, the previous estimation error and a uniformly small error term. The main contribution is the analysis of the solution of the stochastic recursion equation as a fixed point, and the results that the normalized estimation errors are tight and are close to a linear function of the kernel, thus providing a stochastic expansion of the estimators, which is the same as for the Huber-skip. This implies that the iterated estimator is a close approximation of the Huber-skip

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Asymptotic Analysis of the Forward Search

Cited by 2 publications

References 27 publications

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

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

Outlier Detection in Regression Using an Iterated One-Step Approximation to the Huber-Skip Estimator

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