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Statistical Methods for Medicine and Health Sciences

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 15068

Special Issue Editor

Medical Informatics and Data Analysis Research Group, University of Oulu, P.O. Box 5000, FI-90014 Oulu, Finland
Interests: medical statistics; data informatics; statistics in medical journals; statistical computing; statistical modelling; data presentation; bibliometrics; information retrieval
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to bring together information on established statistical methods and their applications in epidemiology, health sciences, dentistry and clinical medicine. Such resources would provide support for researchers at all levels in these fields, as well as for students. It will publish articles on a wide array of methods, consider how these methods should be applied, and provide examples of application in practice.

Statistics play essential roles in knowledge-based medicine and health sciences. Research ranges widely from formulating questions, designing studies, and collecting and analyzing data to interpreting, reporting, and presenting study findings. A high proportion of medical articles are essentially statistical in their presentation. Regardless of the quality of the data and the variables chosen to express the results, the overt evidence of the research is produced as the lists of numbers, tables, plots, graphs, and other displays, i.e., the descriptive statistics. The communication of the descriptive statistics is combined with statistical inference procedures. Statistical review has also become an important and integral part of the editorial process. Because of the increasing dependence on the medical literature, it is essential to collect new ideas and describe promising data analysis methods for medicine, dentistry and healthcare to support understanding of new research findings.

This Special Issue is an opportunity for the scientific community to present research on the application of statistical methods in medicine and health sciences. Both original research and review articles will be accepted in this issue.

Prof. Dr. Pentti Nieminen
Guest Editor

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 submissions that pass pre-check are 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. Entropy is an international peer-reviewed open access monthly 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 2600 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

  • Data analysis
  • Statistical methods
  • Statistical reporting
  • Data presentation
  • Epidemiology
  • Public health
  • Clinical research
  • Health care
  • Methodology
  • Publications

Published Papers (7 papers)

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Research

24 pages, 2302 KiB  
Article
The Role of Entropy in Construct Specification Equations (CSE) to Improve the Validity of Memory Tests: Extension to Word Lists
by Jeanette Melin, Stefan Cano, Agnes Flöel, Laura Göschel and Leslie Pendrill
Entropy 2022, 24(7), 934; https://0-doi-org.brum.beds.ac.uk/10.3390/e24070934 - 05 Jul 2022
Cited by 8 | Viewed by 1261
Abstract
Metrological methods for word learning list tests can be developed with an information theoretical approach extending earlier simple syntax studies. A classic Brillouin entropy expression is applied to the analysis of the Rey’s Auditory Verbal Learning Test RAVLT (immediate recall), where more ordered [...] Read more.
Metrological methods for word learning list tests can be developed with an information theoretical approach extending earlier simple syntax studies. A classic Brillouin entropy expression is applied to the analysis of the Rey’s Auditory Verbal Learning Test RAVLT (immediate recall), where more ordered tasks—with less entropy—are easier to perform. The findings from three case studies are described, including 225 assessments of the NeuroMET2 cohort of persons spanning a cognitive spectrum from healthy older adults to patients with dementia. In the first study, ordinality in the raw scores is compensated for, and item and person attributes are separated with the Rasch model. In the second, the RAVLT IR task difficulty, including serial position effects (SPE), particularly Primacy and Recency, is adequately explained (Pearson’s correlation R=0.80) with construct specification equations (CSE). The third study suggests multidimensionality is introduced by SPE, as revealed through goodness-of-fit statistics of the Rasch analyses. Loading factors common to two kinds of principal component analyses (PCA) for CSE formulation and goodness-of-fit logistic regressions are identified. More consistent ways of defining and analysing memory task difficulties, including SPE, can maintain the unique metrological properties of the Rasch model and improve the estimates and understanding of a person’s memory abilities on the path towards better-targeted and more fit-for-purpose diagnostics. Full article
(This article belongs to the Special Issue Statistical Methods for Medicine and Health Sciences)
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10 pages, 349 KiB  
Article
Unsupervised Hierarchical Classification Approach for Imprecise Data in the Breast Cancer Detection
by Mario Fordellone and Paolo Chiodini
Entropy 2022, 24(7), 926; https://0-doi-org.brum.beds.ac.uk/10.3390/e24070926 - 03 Jul 2022
Cited by 2 | Viewed by 1291
Abstract
(1) Background: in recent years, a lot of the research of statistical methods focused on the classification problem in presence of imprecise data. A particular case of imprecise data is the interval-valued data. Following this research line, in this work a new hierarchical [...] Read more.
(1) Background: in recent years, a lot of the research of statistical methods focused on the classification problem in presence of imprecise data. A particular case of imprecise data is the interval-valued data. Following this research line, in this work a new hierarchical classification technique for multivariate interval-valued data is suggested for diagnosis of the breast cancer; (2) Methods: an unsupervised hierarchical classification method for imprecise multivariate data (called HC-ID) is performed for diagnosis of breast cancer (i.e., to discriminate between benign or malignant masses) and the results have been compared with the conventional (unsupervised) hierarchical classification approach (HC); (3) Results: the application on real data shows that the HC-ID procedure performs better HC procedure in terms of accuracy (HC-ID = 0.80, HC = 0.66) and sensitivity (HC-ID = 0.61, HC = 0.08). In the results obtained by the usual procedure, there is a high degree of false-negative (i.e., benign cancer diagnosis in malignant status) affected by the high degree of variability (i.e., uncertainty) characterizing the worst data. Full article
(This article belongs to the Special Issue Statistical Methods for Medicine and Health Sciences)
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18 pages, 1826 KiB  
Article
MCMCINLA Estimation of Missing Data and Its Application to Public Health Development in China in the Post-Epidemic Era
by Jiaqi Teng, Shuzhen Ding, Xiaoping Shi, Huiguo Zhang and Xijian Hu
Entropy 2022, 24(7), 916; https://0-doi-org.brum.beds.ac.uk/10.3390/e24070916 - 30 Jun 2022
Cited by 1 | Viewed by 1258
Abstract
Medical data are often missing during epidemiological surveys and clinical trials. In this paper, we propose the MCMCINLA estimation method to account for missing data. We introduce a new latent class into the spatial lag model (SLM) and use a conditional autoregressive specification [...] Read more.
Medical data are often missing during epidemiological surveys and clinical trials. In this paper, we propose the MCMCINLA estimation method to account for missing data. We introduce a new latent class into the spatial lag model (SLM) and use a conditional autoregressive specification (CAR) spatial model-based approach to impute missing values, making the model fit into the integrated nested Laplace approximation (INLA) framework. Combining the advantages of both the Markov chain Monte Carlo (MCMC) and INLA frameworks, the MCMCINLA algorithm is used to implement imputation of the missing data and fit the model to derive estimates of the parameters from the posterior margins. Finally, the economic data and the hemorrhagic fever with renal syndrome (HFRS) disease data of mainland China from 2016–2018 are used as examples to explore the development of public health in China in the post-epidemic era. The results show that compared with expectation maximization (EM) and full information maximum likelihood estimation (FIML), the predicted values of the missing data obtained using our method are closer to the true values, and the spatial distribution of HFRS in China can be inferred from the imputation results with a southern-heavy and northern-light distribution. It can provide some references for the development of public health in China in the post-epidemic era. Full article
(This article belongs to the Special Issue Statistical Methods for Medicine and Health Sciences)
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14 pages, 773 KiB  
Article
A Transnational and Transregional Study of the Impact and Effectiveness of Social Distancing for COVID-19 Mitigation
by Tarcísio M. Rocha Filho, Marcelo A. Moret and José F. F. Mendes
Entropy 2021, 23(11), 1530; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111530 - 18 Nov 2021
Cited by 4 | Viewed by 1676
Abstract
We present an analysis of the relationship between SARS-CoV-2 infection rates and a social distancing metric from data for all the states and most populous cities in the United States and Brazil, all the 22 European Economic Community countries and the United Kingdom. [...] Read more.
We present an analysis of the relationship between SARS-CoV-2 infection rates and a social distancing metric from data for all the states and most populous cities in the United States and Brazil, all the 22 European Economic Community countries and the United Kingdom. We discuss why the infection rate, instead of the effective reproduction number or growth rate of cases, is a proper choice to perform this analysis when considering a wide span of time. We obtain a strong Spearman’s rank order correlation between the social distancing metric and the infection rate in each locality. We show that mask mandates increase the values of Spearman’s correlation in the United States, where a mandate was adopted. We also obtain an explicit numerical relation between the infection rate and the social distancing metric defined in the present work. Full article
(This article belongs to the Special Issue Statistical Methods for Medicine and Health Sciences)
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32 pages, 727 KiB  
Article
Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications
by Haoyang Teng and Zhengjun Zhang
Entropy 2021, 23(11), 1517; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111517 - 15 Nov 2021
Cited by 2 | Viewed by 1780
Abstract
Logistic regression is widely used in the analysis of medical data with binary outcomes to study treatment effects through (absolute) treatment effect parameters in the models. However, the indicative parameters of relative treatment effects are not introduced in logistic regression models, which can [...] Read more.
Logistic regression is widely used in the analysis of medical data with binary outcomes to study treatment effects through (absolute) treatment effect parameters in the models. However, the indicative parameters of relative treatment effects are not introduced in logistic regression models, which can be a severe problem in efficiently modeling treatment effects and lead to the wrong conclusions with regard to treatment effects. This paper introduces a new enhanced logistic regression model that offers a new way of studying treatment effects by measuring the relative changes in the treatment effects and also incorporates the way in which logistic regression models the treatment effects. The new model, called the Absolute and Relative Treatment Effects (AbRelaTEs) model, is viewed as a generalization of logistic regression and an enhanced model with increased flexibility, interpretability, and applicability in real data applications than the logistic regression. The AbRelaTEs model is capable of modeling significant treatment effects via an absolute or relative or both ways. The new model can be easily implemented using statistical software, with the logistic regression model being treated as a special case. As a result, the classical logistic regression models can be replaced by the AbRelaTEs model to gain greater applicability and have a new benchmark model for more efficiently studying treatment effects in clinical trials, economic developments, and many applied areas. Moreover, the estimators of the coefficients are consistent and asymptotically normal under regularity conditions. In both simulation and real data applications, the model provides both significant and more meaningful results. Full article
(This article belongs to the Special Issue Statistical Methods for Medicine and Health Sciences)
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16 pages, 858 KiB  
Article
Generalized Poisson Hurdle Model for Count Data and Its Application in Ear Disease
by Guoxin Zuo, Kang Fu, Xianhua Dai and Liwei Zhang
Entropy 2021, 23(9), 1206; https://0-doi-org.brum.beds.ac.uk/10.3390/e23091206 - 13 Sep 2021
Cited by 2 | Viewed by 2757
Abstract
For count data, though a zero-inflated model can work perfectly well with an excess of zeroes and the generalized Poisson model can tackle over- or under-dispersion, most models cannot simultaneously deal with both zero-inflated or zero-deflated data and over- or under-dispersion. Ear diseases [...] Read more.
For count data, though a zero-inflated model can work perfectly well with an excess of zeroes and the generalized Poisson model can tackle over- or under-dispersion, most models cannot simultaneously deal with both zero-inflated or zero-deflated data and over- or under-dispersion. Ear diseases are important in healthcare, and falls into this kind of count data. This paper introduces a generalized Poisson Hurdle model that work with count data of both too many/few zeroes and a sample variance not equal to the mean. To estimate parameters, we use the generalized method of moments. In addition, the asymptotic normality and efficiency of these estimators are established. Moreover, this model is applied to ear disease using data gained from the New South Wales Health Research Council in 1990. This model performs better than both the generalized Poisson model and the Hurdle model. Full article
(This article belongs to the Special Issue Statistical Methods for Medicine and Health Sciences)
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10 pages, 876 KiB  
Article
The Quality of Statistical Reporting and Data Presentation in Predatory Dental Journals Was Lower Than in Non-Predatory Journals
by Pentti Nieminen and Sergio E. Uribe
Entropy 2021, 23(4), 468; https://0-doi-org.brum.beds.ac.uk/10.3390/e23040468 - 16 Apr 2021
Cited by 9 | Viewed by 3389
Abstract
Proper peer review and quality of published articles are often regarded as signs of reliable scientific journals. The aim of this study was to compare whether the quality of statistical reporting and data presentation differs among articles published in ‘predatory dental journals’ and [...] Read more.
Proper peer review and quality of published articles are often regarded as signs of reliable scientific journals. The aim of this study was to compare whether the quality of statistical reporting and data presentation differs among articles published in ‘predatory dental journals’ and in other dental journals. We evaluated 50 articles published in ‘predatory open access (OA) journals’ and 100 clinical trials published in legitimate dental journals between 2019 and 2020. The quality of statistical reporting and data presentation of each paper was assessed on a scale from 0 (poor) to 10 (high). The mean (SD) quality score of the statistical reporting and data presentation was 2.5 (1.4) for the predatory OA journals, 4.8 (1.8) for the legitimate OA journals, and 5.6 (1.8) for the more visible dental journals. The mean values differed significantly (p < 0.001). The quality of statistical reporting of clinical studies published in predatory journals was found to be lower than in open access and highly cited journals. This difference in quality is a wake-up call to consume study results critically. Poor statistical reporting indicates wider general lower quality in publications where the authors and journals are less likely to be critiqued by peer review. Full article
(This article belongs to the Special Issue Statistical Methods for Medicine and Health Sciences)
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