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Mathematics
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Current Research in Biostatistics
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Current Research in Biostatistics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 10526

Special Issue Editors

Dr. Miguel Ángel Montero-Alonso

E-Mail Website
Guest Editor
Department of Statistic and Operational Research, University of Granada, 18016 Granada, Spain
Interests: inference in diagnostic models; biostatistics; statistic elearning; data science; medical statistics
Prof. Dr. Juan De Dios Luna del Castillo

E-Mail Website
Guest Editor
Department of Biostatistic, University of Granada, 18071 Granada, Spain
Interests: predictive models; inference in diagnostic models; biostatistics; scale validation; teaching biostatistics

Special Issue Information

Dear Colleagues,

Biostatistics involves the application of statistics in the biomedical and health sciences. Statistics provides rigorous methodology with which to address typical medical or health problems.

This Special Issue aims to present the developing applications of biostatistics in various areas. Such areas include, but are not limited to:

  • Diagnostic or prognostic models.
  • Causal inference.
  • Analysis of large databases: data fusion.
  • Missing data.
  • Teaching statistics in the health sciences.
  • Stochastic models in biology and health sciences.
  • Spatio-temporal distribution of disease.
  • Measures of association in 2x2 tables.
  • Development of computer packages for biostatistics.
  • Experimental designs.
  • Controlled clinical trails.
  • Information fusion.
  • Combination of observational databases.
  • Estimation of prevalence and incidence under different sampling schemes.

All articles should present a problem statement, the methodology used to solve it and a resolution.

Dr. Miguel Ángel Montero-Alonso
Prof. Dr. Juan De Dios Luna del Castillo
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 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. Mathematics 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 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

  • diagnostic models
  • causal inference
  • computational biostatistics
  • measures of association
  • prognostic models

Published Papers (7 papers)

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Research

17 pages, 440 KiB  
Article
Statistical Considerations for Analyzing Data Derived from Long Longitudinal Cohort Studies
by Rocío Fernández-Iglesias, Pablo Martínez-Camblor, Adonina Tardón and Ana Fernández-Somoano
Mathematics 2023, 11(19), 4070; https://doi.org/10.3390/math11194070 - 25 Sep 2023
Viewed by 667
Abstract
Modern science is frequently based on the exploitation of large volumes of information storage in datasets and involving complex computational architectures. The statistical analyses of these datasets have to cope with specific challenges and frequently involve making informed but arbitrary decisions. Epidemiological papers [...] Read more.
Modern science is frequently based on the exploitation of large volumes of information storage in datasets and involving complex computational architectures. The statistical analyses of these datasets have to cope with specific challenges and frequently involve making informed but arbitrary decisions. Epidemiological papers have to be concise and focused on the underlying clinical or epidemiological results, not reporting the details behind relevant methodological decisions. In this work, we used an analysis of the cardiovascular-related measures tracked in 4–8-year-old children, using data from the INMA-Asturias cohort for illustrating how the decision-making process was performed and its potential impact on the obtained results. We focused on two particular aspects of the problem: how to deal with missing data and which regression model to use to evaluate tracking when there are no defined thresholds to categorize variables into risk groups. As a spoiler, we analyzed the impact on our results of using multiple imputation and the advantage of using quantile regression models in this context. Full article
(This article belongs to the Special Issue Current Research in Biostatistics)
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10 pages, 841 KiB  
Article
Subgroup Identification in Survival Outcome Data Based on Concordance Probability Measurement
by Shengli An, Peter Zhang and Hong-Bin Fang
Mathematics 2023, 11(13), 2855; https://doi.org/10.3390/math11132855 - 26 Jun 2023
Viewed by 741
Abstract
Identifying a subgroup of patients who may have an enhanced treatment effect in a randomized clinical trial has received increasing attention recently. For time-to-event outcomes, it is a challenge to define the effectiveness of a treatment and to choose a cutoff time point [...] Read more.
Identifying a subgroup of patients who may have an enhanced treatment effect in a randomized clinical trial has received increasing attention recently. For time-to-event outcomes, it is a challenge to define the effectiveness of a treatment and to choose a cutoff time point for identifying subgroup membership, especially in trials in which the two treatment arms do not differ in overall survival. In this paper, we propose a mixture cure model to identify a subgroup for a new treatment that was compared to a classical treatment (or placebo) in a randomized clinical trial with respect to survival time. Using the concordance probability measurement (K-index), we propose a statistic to test the existence of subgroups with effective treatments in the treatment arm. Subsequently, the subgroup is defined by a limited number of covariates based on the estimated area under the curve (AUC). The performance of this method in different scenarios is assessed through simulation studies. A real data example is also provided for illustration. Full article
(This article belongs to the Special Issue Current Research in Biostatistics)
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11 pages, 595 KiB  
Article
Scrambling Reports: New Estimators for Estimating the Population Mean of Sensitive Variables
by Pablo O. Juárez-Moreno, Agustín Santiago-Moreno, José M. Sautto-Vallejo and Carlos N. Bouza-Herrera
Mathematics 2023, 11(11), 2572; https://doi.org/10.3390/math11112572 - 04 Jun 2023
Cited by 1 | Viewed by 889
Abstract
Warner proposed a methodology called randomized response techniques, which, through the random scrambling of sensitive variables, allows the non-response rate to be reduced and the response bias to be diminished. In this document, we present a randomized response technique using simple random sampling. [...] Read more.
Warner proposed a methodology called randomized response techniques, which, through the random scrambling of sensitive variables, allows the non-response rate to be reduced and the response bias to be diminished. In this document, we present a randomized response technique using simple random sampling. The scrambling of the sensitive variable is performed through the selection of a report Ri, i = 1,2,3. In order to evaluate the accuracy and efficiency of the proposed estimators, a simulation was carried out with two databases, where the sensitive variables are the destruction of poppy crops in Guerrero, Mexico, and the age at first sexual intercourse. The results show that more accurate estimates are obtained with the proposed model. Full article
(This article belongs to the Special Issue Current Research in Biostatistics)
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18 pages, 857 KiB  
Article
Diagnosing Vascular Aging Based on Macro and Micronutrients Using Ensemble Machine Learning
by Carmen Patino-Alonso, Marta Gómez-Sánchez, Leticia Gómez-Sánchez, Emiliano Rodríguez-Sánchez, Cristina Agudo-Conde, Luis García-Ortiz and Manuel A Gómez-Marcos
Mathematics 2023, 11(7), 1645; https://doi.org/10.3390/math11071645 - 29 Mar 2023
Viewed by 1197
Abstract
The influence of dietary components on vascular dysfunction and aging is unclear. This study therefore aims to propose a model to predict the influence of macro and micronutrients on accelerated vascular aging in a Spanish population without previous cardiovascular disease. This cross-sectional study [...] Read more.
The influence of dietary components on vascular dysfunction and aging is unclear. This study therefore aims to propose a model to predict the influence of macro and micronutrients on accelerated vascular aging in a Spanish population without previous cardiovascular disease. This cross-sectional study involved a total of 501 individuals aged between 35 and 75 years. Carotid-femoral pulse wave velocity (cfPWV) was measured using a Sphygmo Cor® device. Carotid intima-media thickness (IMTc) was measured using a Sonosite Micromax® ultrasound machine. The Vascular Aging Index (VAI) was estimated according to VAI = (LN (1.09) × 10 cIMT + LN (1.14) × cfPWV) 39.1 + 4.76. Vascular aging was defined considering the presence of a vascular lesion and the p75 by age and sex of VAI following two steps: Step 1: subjects were labelled as early vascular aging (EVA) if they had a peripheral arterial disease or carotid artery lesion. Step 2: they were classified as EVA if the VAI value was >p75 and as normal vascular aging (NVA) if it was ≤p75. To predict the model, we used machine learning algorithms to analyse the association between macro and micronutrients and vascular aging. In this article, we proposed the AdXGRA model, a stacked ensemble learning model for diagnosing vascular aging from macro and micronutrients. The proposed model uses four classifiers, AdaBoost (ADB), extreme gradient boosting (XGB), generalized linear model (GLM), and random forest (RF) at the first level, and then combines their predictions by using a second-level multilayer perceptron (MLP) classifier to achieve better performance. The model obtained an accuracy of 68.75% in prediction, with a sensitivity of 66.67% and a specificity of 68.79%. The seven main variables related to EVA in the proposed model were sodium, waist circumference, polyunsaturated fatty acids (PUFA), monounsaturated fatty acids (MUFA), total protein, calcium, and potassium. These results suggest that total protein, PUFA, and MUFA are the macronutrients, and calcium and potassium are the micronutrients related to EVA. Full article
(This article belongs to the Special Issue Current Research in Biostatistics)
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18 pages, 4490 KiB  
Article
DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification
by Mehwish Zafar, Javeria Amin, Muhammad Sharif, Muhammad Almas Anjum, Ghulam Ali Mallah and Seifedine Kadry
Mathematics 2023, 11(2), 364; https://doi.org/10.3390/math11020364 - 10 Jan 2023
Cited by 15 | Viewed by 2393
Abstract
The development of abnormal cell growth is caused by different pathological alterations and some genetic disorders. This alteration in skin cells is very dangerous and life-threatening, and its timely identification is very essential for better treatment and safe cure. Therefore, in the present [...] Read more.
The development of abnormal cell growth is caused by different pathological alterations and some genetic disorders. This alteration in skin cells is very dangerous and life-threatening, and its timely identification is very essential for better treatment and safe cure. Therefore, in the present article, an approach is proposed for skin lesions’ segmentation and classification. So, in the proposed segmentation framework, pre-trained Mobilenetv2 is utilised in the act of the back pillar of the DeepLabv3+ model and trained on the optimum parameters that provide significant improvement for infected skin lesions’ segmentation. The multi-classification of the skin lesions is carried out through feature extraction from pre-trained DesneNet201 with N × 1000 dimension, out of which informative features are picked from the Slim Mould Algorithm (SMA) and input to SVM and KNN classifiers. The proposed method provided a mean ROC of 0.95 ± 0.03 on MED-Node, 0.97 ± 0.04 on PH2, 0.98 ± 0.02 on HAM-10000, and 0.97 ± 0.00 on ISIC-2019 datasets. Full article
(This article belongs to the Special Issue Current Research in Biostatistics)
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15 pages, 2852 KiB  
Article
DExMA: An R Package for Performing Gene Expression Meta-Analysis with Missing Genes
by Juan Antonio Villatoro-García, Jordi Martorell-Marugán, Daniel Toro-Domínguez, Yolanda Román-Montoya, Pedro Femia and Pedro Carmona-Sáez
Mathematics 2022, 10(18), 3376; https://doi.org/10.3390/math10183376 - 17 Sep 2022
Cited by 2 | Viewed by 2149
Abstract
Meta-analysis techniques allow researchers to jointly analyse different studies to determine common effects. In the field of transcriptomics, these methods have gained popularity in recent years due to the increasing number of datasets that are available in public repositories. Despite this, there is [...] Read more.
Meta-analysis techniques allow researchers to jointly analyse different studies to determine common effects. In the field of transcriptomics, these methods have gained popularity in recent years due to the increasing number of datasets that are available in public repositories. Despite this, there is a limited number of statistical software packages that implement proper meta-analysis functionalities for this type of data. This article describes DExMA, an R package that provides a set of functions for performing gene expression meta-analyses, from data downloading to results visualization. Additionally, we implemented functions to control the number of missing genes, which can be a major issue when comparing studies generated with different analytical platforms. DExMA is freely available in the Bioconductor repository. Full article
(This article belongs to the Special Issue Current Research in Biostatistics)
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14 pages, 2310 KiB  
Article
Preliminary Results on the Preinduction Cervix Status by Shear Wave Elastography
by Jorge Torres, María Muñoz, María Del Carmen Porcel, Sofía Contreras, Francisca Sonia Molina, Guillermo Rus, Olga Ocón-Hernández and Juan Melchor
Mathematics 2022, 10(17), 3164; https://doi.org/10.3390/math10173164 - 02 Sep 2022
Cited by 1 | Viewed by 1359
Abstract
The mechanical status of the cervix is a key physiological element during pregnancy. By considering a successful induction when the active phase of labor is achieved, mapping the mechanical properties of the cervix could have predictive potential for the management of induction protocols. [...] Read more.
The mechanical status of the cervix is a key physiological element during pregnancy. By considering a successful induction when the active phase of labor is achieved, mapping the mechanical properties of the cervix could have predictive potential for the management of induction protocols. In this sense, we performed a preliminary assessment of the diagnostic value of using shear wave elastography before labor induction in 54 women, considering the pregnancy outcome and Cesarean indications. Three anatomical cervix regions and standard methods, such as cervical length and Bishop score, were compared. To study the discriminatory power of each diagnostic method, a receiver operating characteristic curve was generated. Differences were observed using the external os region and cervical length in the failure to enter the active phase group compared to the vaginal delivery group (p < 0.05). The area under the ROC curve resulted in 68.9%, 65.2% and 67.2% for external os, internal os and cervix box using elastography, respectively, compared to 69.5% for cervical length and 62.2% for Bishop score. External os elastography values have shown promise in predicting induction success. This a priori information could be used to prepare a study with a larger sample size, which would reduce the effect of any bias selection and increase the predictive power of elastography compared to other classical techniques. Full article
(This article belongs to the Special Issue Current Research in Biostatistics)
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