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- W1805731067 abstract "A parameter estimate from a meta-analysis is biased when the experiments to be combined are a non-random sample from the population of all experiments done on the hypothesis of interest. In particular, publication bias occurs when studies with significant results are more likely to be published than studies with non-significant results. We develop a model for the distribution of the total number of studies carried out, both published and unpublished, dependent on the probability of publication. We assume a selection m-odel where all studies significant at level a: are published, while non-significant studies are published with probability p. Using Metropolis simulation and Gibbs sampling techniques, we generate random samples from the distribution of the total number of studies and study how it changes as p varies. An application on lead exposure and IQ level in children is presented and the results interpreted. Comparisons are made with Rosenthal's fail-safe estimators." @default.
- W1805731067 created "2016-06-24" @default.
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- W1805731067 date "1997-02-24" @default.
- W1805731067 modified "2023-09-27" @default.
- W1805731067 title "Estimating the Number of Unseen Studies in a Meta-Analysis" @default.
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- W1805731067 hasPublicationYear "1997" @default.
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