Continuous outcome logistic regression for analyzing body
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چکیده
Body mass indices (BMIs) are applied to monitor weight status and associated health risks in populations. Binary or multinomial logistic regression models are commonly applied in this context, but are only applicable to BMI values categorized within a small set of defined ad hoc BMI categories. This approach precludes comparisons with studies and models based on different categories. In addition, ad hoc categorization of BMI values prevents the estimation and analysis of the underlying continuous BMI distribution and leads to information loss. As an alternative to multinomial regression following ad hoc categorization, we propose a continuous outcome logistic regression model for the estimation of a continuous BMI distribution. Parameters of interest, such as odds ratios for specific categories, can be extracted from this model post hoc in a general way. A continuous BMI logistic regression that describes BMI distributions avoids the necessity of ad hoc and post hoc category choice and simplifies between-study comparisons and pooling of studies for joint analyses. The method was evaluated empirically using data from the Swiss Health Survey. Torsten Hothorn ( ) Corresponding author: [email protected] : Conceptualization, Data Curation, Formal Analysis, Validation, Visualization, Writing – Original Draft Preparation, Writing – Author roles: Lohse T Review & Editing; : Conceptualization, Funding Acquisition, Supervision, Validation, Writing – Review & Editing; : Rohrmann S Faeh D Conceptualization, Supervision, Validation, Writing – Review & Editing; : Conceptualization, Formal Analysis, Methodology, Software, Hothorn T Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing No competing interests were disclosed. Competing interests: Lohse T, Rohrmann S, Faeh D and Hothorn T. How to cite this article: Continuous outcome logistic regression for analyzing body mass 2017, :1933 (doi: ) index distributions [version 1; referees: awaiting peer review] F1000Research 6 10.12688/f1000research.12934.1 © 2017 Lohse T . This is an open access article distributed under the terms of the , which Copyright: et al Creative Commons Attribution Licence permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. TL, SR and DF were supported by the Swiss Cancer Research foundation (grant no. KFS-3048-08-2012) Grant information: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 01 Nov 2017, :1933 (doi: ) First published: 6 10.12688/f1000research.12934.1 Referee Status: AWAITING PEER REVIEW 01 Nov 2017, :1933 (doi: ) First published: 6 10.12688/f1000research.12934.1 01 Nov 2017, :1933 (doi: ) Latest published: 6 10.12688/f1000research.12934.1 v1 Page 1 of 12 F1000Research 2017, 6:1933 Last updated: 01 NOV 2017
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Continuous outcome logistic regression for analyzing body mass index distributions
Body mass indices (BMIs) are applied to monitor weight status and associated health risks in populations. Binary or multinomial logistic regression models are commonly applied in this context, but are only applicable to BMI values categorized within a small set of defined ad hoc BMI categories. This approach precludes comparisons with studies and models based on different categories. In addi...
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