Header

UZH-Logo

Maintenance Infos

Continuous outcome logistic regression for analyzing body mass index distributions


Lohse, Tina; Rohrmann, Sabine; Faeh, David; Hothorn, Torsten (2017). Continuous outcome logistic regression for analyzing body mass index distributions. F1000Research, 6:1933.

Abstract

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.

Abstract

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.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Downloads

3 downloads since deposited on 19 Jan 2018
3 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2017
Deposited On:19 Jan 2018 11:07
Last Modified:19 Feb 2018 10:25
Publisher:Faculty of 1000 Ltd.
ISSN:2046-1402
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.12688/f1000research.12934.1
PubMed ID:29259768

Download

Download PDF  'Continuous outcome logistic regression for analyzing body mass index distributions'.
Preview
Content: Published Version
Filetype: PDF
Size: 2MB
View at publisher
Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)