Summary:
In a meta-analysis, the unknown parameters are often estimated using maximum
likelihood, and inferences are based on asymptotic theory. It is assumed that,
conditional on study characteristics included in the model, the between-study
distribution and the sampling distributions of the effect sizes are normal. In
practice, however, samples are finite, and the normality assumption may be
violated, possibly resulting in biased estimates and inappropriate standard
errors. In this master thesis, we propose one parametric and two nonparametric
bootstrap methods that can be used to adjust the results of maximum likelihood
estimation in meta-analysis.
Keywords:
Meta-analysis, Fixed effects model, Random effects model, Bootstrap, Simulation