DearBeggMonotonePvalSelection

This function computes a simulation-based \(p\)-value to assess the null hypothesis of no selection. For details we refer
to Rufibach (2011, Section 6).

nonparametric

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).

Kaspar Rufibach

selectMeta

Estimation of Weight Functions in Meta Analysis

DearBeggMonotonePvalSelection function

<dl> <dt>y</dt>
<dd>Normally distributed effect sizes.</dd> <dt>u</dt>
<dd>Associated standard errors.</dd> <dt>theta0</dt>
<dd>Initial estimate for \(\theta\).</dd> <dt>sigma0</dt>
<dd>Initial estimate for \(\sigma\).</dd> <dt>lam</dt>
<dd>Weight of the first entry of \(w\) in the likelihood function. Should be the same as used to generate <code>res</code>.</dd> <dt>M</dt>
<dd>Number of runs to compute \(p\)-value.</dd> <dt>maxiter</dt>
<dd>Maximum number of iterations of differential evolution algorithm. Increase this number to get higher accuracy.</dd> <dt>test.stat</dt>
<dd>A function that takes as argument a vector and returns a number. Defines the test statistic to be used on the estimated
 selection function \(w\).</dd></dl>

Arguments

Kaspar Rufibach (maintainer), <a href="/link/kaspar.rufibach%40gmail.com?package=selectMeta&version=1.0.9" data-mini-rdoc="selectMeta::kaspar.rufibach@gmail.com">kaspar.rufibach@gmail.com</a>, <a href="http://www.kasparrufibach.ch">http://www.kasparrufibach.ch</a>

Author

Compute simulation-based p-value to assess null hypothesis of no selection — DearBeggMonotonePvalSelection

<dl>

 <dt>y</dt>
<dd>Normally distributed effect sizes.</dd>

 <dt>u</dt>
<dd>Associated standard errors.</dd>

 <dt>theta0</dt>
<dd>Initial estimate for \(\theta\).</dd>

 <dt>sigma0</dt>
<dd>Initial estimate for \(\sigma\).</dd>

 <dt>lam</dt>
<dd>Weight of the first entry of \(w\) in the likelihood function. Should be the same as used to generate <code>res</code>.</dd>

 <dt>M</dt>
<dd>Number of runs to compute \(p\)-value.</dd>

 <dt>maxiter</dt>
<dd>Maximum number of iterations of differential evolution algorithm. Increase this number to get higher accuracy.</dd>

 <dt>test.stat</dt>
<dd>A function that takes as argument a vector and returns a number. Defines the test statistic to be used on the estimated
 selection function \(w\).</dd>

</dl>

Kaspar Rufibach (maintainer), <a href='mailto:kaspar.rufibach@gmail.com'>kaspar.rufibach@gmail.com</a>, <a href='http://www.kasparrufibach.ch'>http://www.kasparrufibach.ch</a>

DearBeggMonotonePvalSelection: Compute simulation-based p-value to assess null hypothesis of no selection

Description

Usage

Value

Arguments

Author

References

See Also