Prioritizing covariates in the planning of future studies in the meta-analytic framework
Karvanen, J., & Sillanpää, M. J. (2017). Prioritizing covariates in the planning of future studies in the meta-analytic framework. Biometrical Journal, 59(1), 110-125. https://doi.org/10.1002/bimj.201600067
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Biometrical JournalDate
2017Copyright
© 2016 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim.
Science can be seen as a sequential process where each new study augments
evidence to the existing knowledge. To have the best prospects to make an
impact in this process, a new study should be designed optimally taking into
account the previous studies and other prior information. We propose a formal
approach for the covariate prioritization, i.e., the decision about the covariates
to be measured in a new study. The decision criteria can be based on conditional
power, change of the p-value, change in lower confidence limit, Kullback-Leibler
divergence, Bayes factors, Bayesian false discovery rate or difference between
prior and posterior expectation. The criteria can be also used for decisions on
the sample size. As an illustration, we consider covariate prioritization based on
genome-wide association studies for C-reactive protein levels and make suggestions on the genes to be studied further.
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Wiley - VCH Verlag GmbH & Co. KGaAISSN Search the Publication Forum
0323-3847Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/26269620
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