Package: selectMeta
Type: Package
Title: Estimation of weight functions in meta analysis
Version: 1.0.3
Date: 2011-03-07
Author: Kaspar Rufibach <kaspar.rufibach@ifspm.uzh.ch>
Maintainer: Kaspar Rufibach <kaspar.rufibach@ifspm.uzh.ch>
Depends: DEoptim (>= 2.0-6)
Description: 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).
License: GPL (>= 2)
URL: http://www.biostat.uzh.ch/aboutus/people/rufibach.html
Packaged: 2011-03-09 05:47:10 UTC; rufibach
Repository: CRAN
Date/Publication: 2011-03-09 14:25:31
