selectMeta: Estimation of Weight Functions in Meta Analysis

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Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).

Author
Kaspar Rufibach <kaspar.rufibach@gmail.com>
Date of publication
2015-07-03 12:51:10
Maintainer
Kaspar Rufibach <kaspar.rufibach@gmail.com>
License
GPL (>= 2)
Version
1.0.8
URLs

View on CRAN

Man pages

DearBegg
Compute the nonparametric weight function from Dear and Begg...
DearBeggMonotoneCItheta
Compute an approximate profile likelihood ratio confidence...
DearBeggMonotonePvalSelection
Compute simulation-based p-value to assess null hypothesis of...
education
Dataset open vs. traditional education on creativity
effectBias
Compute bias for each effect size based on estimated weight...
IyenGreen
Compute MLE and weight functions of Iyengar and Greenhouse...
passive_smoking
Dataset on the effect of environmental tobacco smoke
pPool
Pool p-values in pairs
Pval
Functions for the distribution of p-values
selectMeta-package
Estimation of Weight Functions in Meta Analysis
weightLine
Function to plot estimated weight functions

Files in this package

selectMeta
selectMeta/NAMESPACE
selectMeta/NEWS
selectMeta/data
selectMeta/data/education.rda
selectMeta/data/passive_smoking.rda
selectMeta/R
selectMeta/R/dPval.r
selectMeta/R/normalizeT.r
selectMeta/R/rPval.r
selectMeta/R/DearBeggMonotone.r
selectMeta/R/IyenGreenMLE.r
selectMeta/R/DearBeggMonotonePvalSelection.r
selectMeta/R/DearBeggProfileLL.r
selectMeta/R/DearBeggLoglik.r
selectMeta/R/pPool.r
selectMeta/R/DearBeggToMinimizeProfile.r
selectMeta/R/qPval.r
selectMeta/R/IyenGreenLoglikT.r
selectMeta/R/Hij.r
selectMeta/R/pPval.r
selectMeta/R/DearBeggToMinimize.r
selectMeta/R/weightLine.r
selectMeta/R/IyenGreenWeight.r
selectMeta/R/DearBegg.r
selectMeta/R/effectBias.r
selectMeta/R/DearBeggMonotoneCItheta.r
selectMeta/MD5
selectMeta/DESCRIPTION
selectMeta/man
selectMeta/man/Pval.Rd
selectMeta/man/weightLine.Rd
selectMeta/man/DearBeggMonotoneCItheta.Rd
selectMeta/man/DearBeggMonotonePvalSelection.Rd
selectMeta/man/education.Rd
selectMeta/man/effectBias.Rd
selectMeta/man/IyenGreen.Rd
selectMeta/man/passive_smoking.Rd
selectMeta/man/selectMeta-package.Rd
selectMeta/man/pPool.Rd
selectMeta/man/DearBegg.Rd