title: Prioritizing covariates in the planning of future studies in the meta-analytic framework.
abstract: 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, that is 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.
authors.affiliation: Department of Mathematics and Statistics, University of Jyvaskyla, Jyväskylä, Finland.
authors.forename: Juha
authors.initials: J
authors.lastname: Karvanen
authors.affiliation: Department of Mathematical Sciences and Biocenter Oulu, University of Oulu, Oulu, Finland.
authors.forename: Mikko J
authors.initials: MJ
authors.lastname: Sillanpää
mesh_terms: D001499:Bayes Theorem; D002097:C-Reactive Protein / Q000235:genetics; D055106:Genome-Wide Association Study; D015201:Meta-Analysis as Topic; D011336:Probability; D012107:Research Design / Q000639:trends*; D018401:Sample Size
chemical_list: D002097:C-Reactive Protein
keywords: Design; Evidence-based medicine; Meta-analysis; Power; Scientific method