193 research outputs found
MVPBT: R package for publication bias tests in meta-analysis of diagnostic accuracy studies
Meta-analysis for diagnostic test accuracy (DTA) has been a standard research
method for synthesizing evidence from diagnostic studies. In DTA meta-analysis,
although publication bias is an important source of bias, no certain methods
similar to the Egger test in univariate meta-analysis have been developed to
detect such bias. However, several recent studies have discussed these methods
in the framework of multivariate meta-analysis, and some generalized Egger
tests have been developed. The R package MVPBT
(https://cran.r-project.org/web/packages/MVPBT/) was developed to implement the
generalized Egger tests developed by Noma (2020; Biometrics 76, 1255-1259) for
DTA meta-analysis. Noma's publication bias tests effectively incorporate the
correlation information between multiple outcomes and are expected to improve
the statistical powers. The present paper provides a nontechnical introduction
and practical examples of data analyses of the publication bias tests of DTA
meta-analysis using the MVPBT package
Confidence intervals of prediction accuracy measures for multivariable prediction models based on the bootstrap-based optimism correction methods
In assessing prediction accuracy of multivariable prediction models, optimism
corrections are essential for preventing biased results. However, in most
published papers of clinical prediction models, the point estimates of the
prediction accuracy measures are corrected by adequate bootstrap-based
correction methods, but their confidence intervals are not corrected, e.g., the
DeLong's confidence interval is usually used for assessing the C-statistic.
These naive methods do not adjust for the optimism bias and do not account for
statistical variability in the estimation of parameters in the prediction
models. Therefore, their coverage probabilities of the true value of the
prediction accuracy measure can be seriously below the nominal level (e.g.,
95%). In this article, we provide two generic bootstrap methods, namely (1)
location-shifted bootstrap confidence intervals and (2) two-stage bootstrap
confidence intervals, that can be generally applied to the bootstrap-based
optimism correction methods, i.e., the Harrell's bias correction, 0.632, and
0.632+ methods. In addition, they can be widely applied to various methods for
prediction model development involving modern shrinkage methods such as the
ridge and lasso regressions. Through numerical evaluations by simulations, the
proposed confidence intervals showed favourable coverage performances. Besides,
the current standard practices based on the optimism-uncorrected methods showed
serious undercoverage properties. To avoid erroneous results, the
optimism-uncorrected confidence intervals should not be used in practice, and
the adjusted methods are recommended instead. We also developed the R package
predboot for implementing these methods (https://github.com/nomahi/predboot).
The effectiveness of the proposed methods are illustrated via applications to
the GUSTO-I clinical trial
メタアナリシスにおける平均治療効果の推定
Open House, ISM in Tachikawa, 2013.6.14統計数理研究所オープンハウス(立川)、H25.6.14ポスター発
Neyman-Pearson補題の多重検定への拡張
Open House, ISM in Tachikawa, 2012.6.15統計数理研究所オープンハウス(立川)、H24.6.15ポスター発
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