Inference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies
- Submitting institution
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The University of East Anglia
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 182620427
- Type
- D - Journal article
- DOI
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10.1002/bimj.201500115
- Title of journal
- Biometrical Journal
- Article number
- -
- First page
- 896
- Volume
- 58
- Issue
- 4
- ISSN
- 0323-3847
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We investigated biases arising in the estimation of transformed binomial probabilities under the assumptions of random or mixed effects models, and its deleterious effects on inference in a meta-analysis. We demonstrated and quantified these effects in the examples of arcsine and log-odds transformations, for small values of the intracluster correlation coefficient. These biases result in abysmal coverage of the combined effect for large number of studies. As a remedy, we proposed a plug-in bias correction for the arcsine transformation, but it is more difficult to provide a similar bias correction for the log-odds transformation of proportions.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -