A flexible method for aggregation of prior statistical findings
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Rahmandad-2017-A flexible method for aggregati.pdf
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Author(s)
Paynabar, Kamran
Rahmandad, Hazhir
Jalali, Seyed Mohammad Javad
Date Issued
April 2017
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Rahmandad, Hazhir; Jalali, Mohammad S. and Paynabar, Kamran. “A Flexible Method for Aggregation of Prior Statistical Findings.” Edited by Xi Luo. PLOS ONE 12, no. 4 (April 2017): e0175111 © 2017 Rahmandad et al
Version
Final published version
Abstract
Rapid growth in scientific output requires methods for quantitative synthesis of prior research, yet current meta-analysis methods limit aggregation to studies with similar designs. Here we describe and validate Generalized Model Aggregation (GMA), which allows researchers to combine prior estimated models of a phenomenon into a quantitative meta-model, while imposing few restrictions on the structure of prior models or on the meta-model. In an empirical validation, building on 27 published equations from 16 studies, GMA provides a predictive equation for Basal Metabolic Rate that outperforms existing models, identifies novel nonlinearities, and estimates biases in various measurement methods. Additional numerical examples demonstrate the ability of GMA to obtain unbiased estimates from potentially mis-specified prior studies. Thus, in various domains, GMA can leverage previous findings to compare alternative theories, advance new models, and assess the reliability of prior studies, extending meta-analysis toolbox to many new problems.
MIT Department
Sloan School of Management
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Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0175111
