BiMM tree: a decision tree method for modeling clustered and longitudinal binary outcomes
| Autor: | Bethany J. Wolf, Constantine J. Karvellas, David G. Koch, Valerie Durkalski, Jaime L. Speiser, Dongjun Chung |
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| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Mixed model 021103 operations research 0211 other engineering and technologies Decision tree Liver failure Binary number 02 engineering and technology Standard methods computer.software_genre 01 natural sciences Article Generalized linear mixed model 010104 statistics & probability Tree (data structure) Modeling and Simulation Statistics A priori and a posteriori Data mining 0101 mathematics computer Mathematics |
| Zdroj: | Commun Stat Simul Comput |
| ISSN: | 1532-4141 0361-0918 |
| DOI: | 10.1080/03610918.2018.1490429 |
| Popis: | Clustered binary outcomes are frequently encountered in clinical research (e.g. longitudinal studies). Generalized linear mixed models (GLMMs) for clustered endpoints have challenges for some scenarios (e.g. data with multi-way interactions and nonlinear predictors unknown a priori). We develop an alternative, data-driven method called Binary Mixed Model (BiMM) tree, which combines decision tree and GLMM within a unified framework. Simulation studies show that BiMM tree achieves slightly higher or similar accuracy compared to standard methods. The method is applied to a real dataset from the Acute Liver Failure Study Group. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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