Grid multi-category response logistic models.

TitleGrid multi-category response logistic models.
Publication TypeJournal Article
Year of Publication2015
AuthorsWu, Y, Jiang, X, Wang, S, Jiang, W, Li, P, Ohno-Machado, L
JournalBMC Med Inform Decis Mak
Date Published2015
iDASH CategoryPrivacy Technology
Abstract<p><b>BACKGROUND: </b>Multi-category response models are very important complements to binary logistic models in medical decision-making. Decomposing model construction by aggregating computation developed at different sites is necessary when data cannot be moved outside institutions due to privacy or other concerns. Such decomposition makes it possible to conduct grid computing to protect the privacy of individual observations.</p><p><b>METHODS: </b>This paper proposes two grid multi-category response models for ordinal and multinomial logistic regressions. Grid computation to test model assumptions is also developed for these two types of models. In addition, we present grid methods for goodness-of-fit assessment and for classification performance evaluation.</p><p><b>RESULTS: </b>Simulation results show that the grid models produce the same results as those obtained from corresponding centralized models, demonstrating that it is possible to build models using multi-center data without losing accuracy or transmitting observation-level data. Two real data sets are used to evaluate the performance of our proposed grid models.</p><p><b>CONCLUSIONS: </b>The grid fitting method offers a practical solution for resolving privacy and other issues caused by pooling all data in a central site. The proposed method is applicable for various likelihood estimation problems, including other generalized linear models.</p>
Alternate JournalBMC Med Inform Decis Mak
PubMed ID25886151
PubMed Central IDPMC4342889
Grant ListK99 HG008175 / HG / NHGRI NIH HHS / United States
K99HG008175 / HG / NHGRI NIH HHS / United States
R00 LM011392 / LM / NLM NIH HHS / United States
R00LM011392 / LM / NLM NIH HHS / United States
R21LM012060 / LM / NLM NIH HHS / United States
U54HL108460 / HL / NHLBI NIH HHS / United States

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