Special Issue "Statistical and Epidemiological Methods in Public Health"

Special Issue Editors

Dr. Sören Möller
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Guest Editor
Open Patient data Explorative Network, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
Interests: biostatistics; epidemiological methods; risk prediction scores; unusual bias sources; probability theory
Dr. Linda Juel Ahrenfeldt
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Assistant Guest Editor
Unit of Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, 5000 Odense, Denmark
Interests: sex differences in health and mortality; aging; twin research

Special Issue Information

Dear Colleagues,

Epidemiology has been the cornerstone of public health research for decades and has come even more to the center of health research and public awareness during the 2020 COVID-19 pandemic. Thus, it is of utmost importance that epidemiological studies investigating issues relevant to public health are carried out by applying correct and state-of-the-art statistical and epidemiological methods to ensure the validity of scientific results and relevance of resulting public health interventions.

The purpose of this Special Issue is to provide a collection of articles that describe, evaluate, or discuss modern statistical and epidemiological methods relevant to research in public health with the aim of evolving the area toward an even higher degree of epidemiological and statistical rigor. Articles providing theoretical considerations, practical applications, best practice recommendations, and pedagogical advice are welcome.

Dr. Sören Möller
Dr. Linda Juel Ahrenfeldt
Guest Editors

Manuscript Submission Information

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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

  • biostatistics
  • epidemiology
  • public health
  • statistical methods
  • epidemiological methods
  • bias

Published Papers (2 papers)

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Research

Open AccessArticle
Variability Matters
Int. J. Environ. Res. Public Health 2021, 18(1), 157; https://doi.org/10.3390/ijerph18010157 - 28 Dec 2020
Viewed by 590
Abstract
Much of science, including public health research, focuses on means (averages). The purpose of the present paper is to reinforce the idea that variability matters just as well. At the hand of four examples, we highlight four classes of situations where the conclusion [...] Read more.
Much of science, including public health research, focuses on means (averages). The purpose of the present paper is to reinforce the idea that variability matters just as well. At the hand of four examples, we highlight four classes of situations where the conclusion drawn on the basis of the mean alone is qualitatively altered when variability is also considered. We suggest that some of the more serendipitous results have their origin in variability. Full article
(This article belongs to the Special Issue Statistical and Epidemiological Methods in Public Health)
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Open AccessArticle
Nonparametric Limits of Agreement in Method Comparison Studies: A Simulation Study on Extreme Quantile Estimation
Int. J. Environ. Res. Public Health 2020, 17(22), 8330; https://doi.org/10.3390/ijerph17228330 - 11 Nov 2020
Viewed by 432
Abstract
Bland–Altman limits of agreement and the underlying plot are a well-established means in method comparison studies on quantitative outcomes. Normally distributed paired differences, a constant bias, and variance homogeneity across the measurement range are implicit assumptions to this end. Whenever these assumptions are [...] Read more.
Bland–Altman limits of agreement and the underlying plot are a well-established means in method comparison studies on quantitative outcomes. Normally distributed paired differences, a constant bias, and variance homogeneity across the measurement range are implicit assumptions to this end. Whenever these assumptions are not fully met and cannot be remedied by an appropriate transformation of the data or the application of a regression approach, the 2.5% and 97.5% quantiles of the differences have to be estimated nonparametrically. Earlier, a simple Sample Quantile (SQ) estimator (a weighted average of the observations closest to the target quantile), the Harrell–Davis estimator (HD), and estimators of the Sfakianakis–Verginis type (SV) outperformed 10 other quantile estimators in terms of mean coverage for the next observation in a simulation study, based on sample sizes between 30 and 150. Here, we investigate the variability of the coverage probability of these three and another three promising nonparametric quantile estimators with n=50(50)200,250(250)1000. The SQ estimator outperformed the HD and SV estimators for n=50 and was slightly better for n=100, whereas the SQ, HD, and SV estimators performed identically well for n150. The similarity of the boxplots for the SQ estimator across both distributions and sample sizes was striking. Full article
(This article belongs to the Special Issue Statistical and Epidemiological Methods in Public Health)
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