Special Issue "Statistical Modelling in Health Research: Best Practices for Description, Explanation and Prediction"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Public Health Statistics and Risk Assessment".

Deadline for manuscript submissions: 31 December 2022.

Special Issue Editors

Dr. Georg Heinze
E-Mail Website
Guest Editor
Center for Medical Statistics, Informatics and Intelligent Systems, Institute of Clinical Biometrics, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
Interests: statistical modeling; prediction; observational studies; clinical epidemiology
Dr. Marianne Huebner
E-Mail Website
Guest Editor
Department of Statistics and Probability, Michigan State University, 619 Red Cedar Rd, East Lansing, MI 48824, USA
Interests: modeling health outcomes; sports medicine; health education; initial data analysis; statistical genomics in cancer studies
Dr. Susanne Strohmaier
E-Mail Website
Guest Editor
Center for Public Health, Department of Epidemiology, Medical University of Vienna, Kinderspitalgasse 15, 1090 Vienna, Austria
Interests: epidemiological methods; causal inference; time-to-event analysis

Special Issue Information

Dear Colleagues,

The validity and practical utility of observational studies in environmental research and public health depend critically on appropriate statistical methods for modeling data and accurate interpretation of results. Statistical methodology for model building has seen substantial development; however, many of these developments have not been exploited to their full potential or are ignored in practice. Having a clear research aim, identifying it as descriptive, predictive, or explanatory, and matching the statistical models to the health research context are the basis for choosing an appropriate statistical model and interpreting the findings correctly.

For this Special Issue, we encourage submissions on methodology applications of statistical modeling that have the potential to improve the practice of research and education of environmental and public health researchers. In particular, we expect emphasis to be placed on the usefulness of the proposed methodology for descriptive, predictive, or explanatory research aims in topics of environmental health sciences and public health. We also invite submissions on independent comparison studies of methodologies, such as simulation studies (including protocols of such studies) or studies where competing methodologies are applied to health data to learn about their properties, advantages, or disadvantages. Submissions could include educative review papers on state-of-the-art statistical methodology or teaching examples derived from health studies aiming to support statistical education and training of environmental and public health scientists. Ideally, any data sets used should be made publicly available.

Dr. Georg Heinze
Dr. Marianne Huebner
Dr. Susanne Strohmaier
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • statistical modeling
  • environmental health sciences
  • public health
  • description
  • explanation
  • prediction
  • confounding
  • validation
  • causal inference.

Published Papers (1 paper)

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Research

Article
Regression with Highly Correlated Predictors: Variable Omission Is Not the Solution
Int. J. Environ. Res. Public Health 2021, 18(8), 4259; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18084259 - 17 Apr 2021
Viewed by 808
Abstract
Regression models have been in use for decades to explore and quantify the association between a dependent response and several independent variables in environmental sciences, epidemiology and public health. However, researchers often encounter situations in which some independent variables exhibit high bivariate correlation, [...] Read more.
Regression models have been in use for decades to explore and quantify the association between a dependent response and several independent variables in environmental sciences, epidemiology and public health. However, researchers often encounter situations in which some independent variables exhibit high bivariate correlation, or may even be collinear. Improper statistical handling of this situation will most certainly generate models of little or no practical use and misleading interpretations. By means of two example studies, we demonstrate how diagnostic tools for collinearity or near-collinearity may fail in guiding the analyst. Instead, the most appropriate way of handling collinearity should be driven by the research question at hand and, in particular, by the distinction between predictive or explanatory aims. Full article
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