Special Issue "Applied Medical Statistics: Theory, Computation, Applicability"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics Theory".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Prof. Dr. Sorana D. Bolboacă
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Guest Editor
Department of Medical Informatics and Biostatistics, "Iuliu Haţieganu" University of Medicine and Pharmacy, Louis Pasteur Str., No. 6, 400349 Cluj-Napoca, Cluj, Romania
Interests: applied and computational statistics; molecular modeling; genetic analysis; statistical modeling in medicine; integrated health informatics system; medical diagnostic research; statistical inference; medical imaging analysis; assisted decision systems; research ethics; social media and health information; evidence-based medicine
Special Issues and Collections in MDPI journals
Ass. Prof. Dr. Polychronis Kostoulas
E-Mail Website1 Website2
Guest Editor
Laboratory of Epidemiology, Faculty of Public Health & Faculty of Veterinary Medicine, University of Thessaly, Karditsa, 224 Trikalon st., Greece
Interests: epidemiology; Bayesian latent class models for the evaluation of diagnostic tests; prevalence estimation and proof of disease freedom; deep learning applications in diagnostics

Special Issue Information

Dear Colleagues,

Scientific knowledge in medicine is supported by a reproducible design of experiment and a valid and reliable statistical analysis. Both components are a ‘must’ since scientific research in medicine aims to improve medical outcomes. Statistical methods need proper understanding by researchers as well as readers to produce the expected outcome, namely successful medical diagnosis and treatment. Considerable progress in medical statistics was made during the last decade, and many methods exist but they are too complex to be easily understood by physicians.

The purpose of the Applied Medical Statistics: Theory, Computation, Applicability Special Issue is to gather under the same umbrella statistical guidelines as simple and structured tools to be easily understood by a physician and replicated by researchers. The manuscripts must clearly and easily explain information about what the methods are, how they work, and their clinical applicability, as instruments for critical appraisal of medical scientific literature. Statistical methods and applications in medicine, dental medicine, and veterinary medicine will be appreciated.

Prof. Dr. Sorana D. Bolboacă
Ass. Prof. Dr. Polychronis Kostoulas
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. 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 1600 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 data analyses
  • Statistical guideline
  • Statistical data interpretation
  • Computing statistics
  • Bayesian true prevalence estimation of disease

Published Papers (5 papers)

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Research

Open AccessArticle
Use of Bayesian Markov Chain Monte Carlo Methods to Model Kuwait Medical Genetic Center Data: An Application to Down Syndrome and Mental Retardation
Mathematics 2021, 9(3), 248; https://doi.org/10.3390/math9030248 - 27 Jan 2021
Viewed by 479
Abstract
Logit, probit and complementary log-log models are the most widely used models when binary dependent variables are available. Conventionally, these models have been frequentists. This paper aims to demonstrate how such models can be implemented relatively quickly and easily from a Bayesian framework [...] Read more.
Logit, probit and complementary log-log models are the most widely used models when binary dependent variables are available. Conventionally, these models have been frequentists. This paper aims to demonstrate how such models can be implemented relatively quickly and easily from a Bayesian framework using Gibbs sampling Markov chain Monte Carlo simulation methods in WinBUGS. We focus on the modeling and prediction of Down syndrome (DS) and Mental retardation (MR) data from an observational study at Kuwait Medical Genetic Center over a 30-year time period between 1979 and 2009. Modeling algorithms were used in two distinct ways; firstly, using three different methods at the disease level, including logistic, probit and cloglog models, and, secondly, using bivariate logistic regression to study the association between the two diseases in question. The models are compared in terms of their predictive ability via R2, adjusted R2, root mean square error (RMSE) and Bayesian Deviance Information Criterion (DIC). In the univariate analysis, the logistic model performed best, with R2 (0.1145), adjusted R2 (0.114), RMSE (0.3074) and DIC (7435.98) for DS, and R2 (0.0626), adjusted R2 (0.0621), RMSE (0.4676) and DIC (23120) for MR. In the bivariate case, results revealed that 7 and 8 out of the 10 selected covariates were significantly associated with DS and MR respectively, whilst none were associated with the interaction between the two outcomes. Bayesian methods are more flexible in handling complex non-standard models as well as they allow model fit and complexity to be assessed straightforwardly for non-nested hierarchical models. Full article
(This article belongs to the Special Issue Applied Medical Statistics: Theory, Computation, Applicability)
Open AccessArticle
Wavelet Transform-Statistical Time Features-Based Methodology for Epileptic Seizure Prediction Using Electrocardiogram Signals
Mathematics 2020, 8(12), 2125; https://doi.org/10.3390/math8122125 - 27 Nov 2020
Cited by 1 | Viewed by 536
Abstract
Epilepsy is a brain disorder that affects about 50 million persons around the world and is characterized by generating recurrent seizures, which can put patients in permanent because of falls, drowning, burns, and prolonged seizures that they can suffer. Hence, it is of [...] Read more.
Epilepsy is a brain disorder that affects about 50 million persons around the world and is characterized by generating recurrent seizures, which can put patients in permanent because of falls, drowning, burns, and prolonged seizures that they can suffer. Hence, it is of vital importance to propose a methodology with the capability of predicting a seizure with several minutes before the onset, allowing that the patients take their precautions against injuries. In this regard, a methodology based on the wavelet packet transform (WPT), statistical time features (STFs), and a decision tree classifier (DTC) for predicting an epileptic seizure using electrocardiogram (ECG) signals is presented. Seventeen STFs were analyzed to measure changes in the properties of ECG signals and find characteristics capable of differentiating between healthy and 15 min prior to seizure signals. The effectiveness of the proposed methodology for predicting an epileptic event is demonstrated using a database of seven patients with 10 epileptic seizures, which was provided by the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH). The results show that the proposed methodology is capable of predicting an epileptic seizure 15 min before with an accuracy of 100%. Our results suggest that the use of STFs at frequency bands related to heart activity to find parameters for the prediction of epileptic seizures is suitable. Full article
(This article belongs to the Special Issue Applied Medical Statistics: Theory, Computation, Applicability)
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Open AccessArticle
Receiver Operating Characteristic Prediction for Classification: Performances in Cross-Validation by Example
Mathematics 2020, 8(10), 1741; https://doi.org/10.3390/math8101741 - 10 Oct 2020
Cited by 1 | Viewed by 592
Abstract
The stability of receiver operating characteristic in context of random split used in development and validation sets, as compared to the full models for three inflammatory ratios (neutrophil-to-lymphocyte (NLR), derived neutrophil-to-lymphocyte (dNLR) and platelet-to-lymphocyte (PLR) ratio) evaluated as predictors for metastasis in patients [...] Read more.
The stability of receiver operating characteristic in context of random split used in development and validation sets, as compared to the full models for three inflammatory ratios (neutrophil-to-lymphocyte (NLR), derived neutrophil-to-lymphocyte (dNLR) and platelet-to-lymphocyte (PLR) ratio) evaluated as predictors for metastasis in patients with colorectal cancer, was investigated. Data belonging to patients admitted with the diagnosis of colorectal cancer from January 2014 until September 2019 in a single hospital were used. There were 1688 patients eligible for the study, 418 in the metastatic stage. All investigated inflammatory ratios proved to be significant classification models on both the full models and on cross-validations (AUCs > 0.05). High variability of the cut-off values was observed in the unrestricted and restricted split (full models: 4.255 for NLR, 2.745 for dNLR and 255.56 for PLR; random splits: cut-off from 3.215 to 5.905 for NLR, from 2.625 to 3.575 for dNLR and from 134.67 to 335.9 for PLR), but with no effect on the models characteristics or performances. The investigated biomarkes proved limited value as predictors for metastasis (AUCs < 0.8), with largely sensitivity and specificity (from 33.3% to 79.2% for the full model and 29.1% to 82.7% in the restricted splits). Our results showed that a simple random split of observations, weighting or not the patients with and whithout metastasis, in a ROC analysis assures the performances similar to the full model, if at least 70% of the available population is included in the study. Full article
(This article belongs to the Special Issue Applied Medical Statistics: Theory, Computation, Applicability)
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Open AccessArticle
Definition and Estimation of Covariate Effect Types in the Context of Treatment Effectiveness
Mathematics 2020, 8(10), 1657; https://doi.org/10.3390/math8101657 - 25 Sep 2020
Viewed by 405
Abstract
In some clinical studies, assessing covariate effect types indicating whether a covariate is predictive and/or prognostic is of interest, in addition to the study endpoint. Recently, for a case with a binary outcome, Chiba (Clinical Trials, 2019; 16: 237–245) proposed the new concept [...] Read more.
In some clinical studies, assessing covariate effect types indicating whether a covariate is predictive and/or prognostic is of interest, in addition to the study endpoint. Recently, for a case with a binary outcome, Chiba (Clinical Trials, 2019; 16: 237–245) proposed the new concept of covariate effect type, which is assessed in terms of four response types, and showed that standard subgroup or regression analysis is applicable only in certain cases. Although this concept could be useful for supplementing conventional standard analysis, its application is limited to cases with a binary outcome. In this article, we aim to generalize Chiba’s concept to continuous and time-to-event outcomes. We define covariate effect types based on four response types. It is difficult to estimate the response types from the observed data without making certain assumptions, so we propose a simple method to estimate them under the assumption of independent potential outcomes. Our approach is illustrated using data from a clinical study with a time-to-event outcome. Full article
(This article belongs to the Special Issue Applied Medical Statistics: Theory, Computation, Applicability)
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Open AccessArticle
Common Medical and Statistical Problems: The Dilemma of the Sample Size Calculation for Sensitivity and Specificity Estimation
Mathematics 2020, 8(8), 1258; https://doi.org/10.3390/math8081258 - 01 Aug 2020
Viewed by 539
Abstract
Sample size calculation in biomedical practice is typically based on the problematic Wald method for a binomial proportion, with potentially dangerous consequences. This work highlights the need of incorporating the concept of conditional probability in sample size determination to avoid reduced sample sizes [...] Read more.
Sample size calculation in biomedical practice is typically based on the problematic Wald method for a binomial proportion, with potentially dangerous consequences. This work highlights the need of incorporating the concept of conditional probability in sample size determination to avoid reduced sample sizes that lead to inadequate confidence intervals. Therefore, new definitions are proposed for coverage probability and expected length of confidence intervals for conditional probabilities, like sensitivity and specificity. The new definitions were used to assess seven confidence interval estimation methods. In order to determine the sample size, two procedures—an optimal one, based on the new definitions, and an approximation—were developed for each estimation method. Our findings confirm the similarity of the approximated sample sizes to the optimal ones. R code is provided to disseminate these methodological advances and translate them into biomedical practice. Full article
(This article belongs to the Special Issue Applied Medical Statistics: Theory, Computation, Applicability)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Definition and Estimation of Covariate Effect Types for Treatment Effectiveness
Authors: Yasutaka Chiba
Affiliation: Clinical Research Center, Kinki University Hospital 377-2, Ohno-higashi, Osakasayama, Osaka 589-8511, Japan
Abstract: In some clinical studies, researchers are interested in assessing covariate effect types that indicate whether a covariate is predictive and/or prognostic, in addition to endpoint evaluation. Recently, for a case with a binary outcome, Chiba (Clinical Trials, 2019; 16: 237-245) proposed a new concept of the covariate effect type, which is assessed in terms of four response types of potential outcomes, and showed that currently standard subgroup or regression analysis, which is assessed by two types, can deal with only a special case. Although his concept can be a potentially valuable alternative to currently standard analysis, its application is limited into a case with a binary outcome. In this article, we aim to generalize his concept to be applicable to continuous and time-to-event outcomes. Following his concept, we define covariate effect types by applying four response types of potential outcomes. As it is difficult to estimate the four types from the observed data without making any assumption, we propose an estimation method under the assumption of independent potential outcomes. Our approach is illustrated using data from a clinical study with a time-to-event outcome.

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