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Article ; Online: Prevalence ratio estimation via logistic regression

LEILA D. AMORIM / RAYDONAL OSPINA

Anais da Academia Brasileira de Ciências, Vol 93, Iss

a tool in R

2021 Volume 4

Abstract: Abstract The interpretation of odds ratios (OR) as prevalence ratios (PR) in cross-sectional studies have been criticized since this equivalence is not true unless under specific circumstances. The logistic regression model is a very well known ... ...

Abstract	Abstract The interpretation of odds ratios (OR) as prevalence ratios (PR) in cross-sectional studies have been criticized since this equivalence is not true unless under specific circumstances. The logistic regression model is a very well known statistical tool for analysis of binary outcomes and frequently used to obtain adjusted OR. Here, we introduce the prLogistic for the R statistical computing environment which can be obtained from The Comprehensive R Archive Network, https://cran.r-project.org/package=prLogistic. The package prLogistic was built to assist the estimation of PR via logistic regression models adjusted by delta method and bootstrap for analysis of independent and correlated binary data. Two applications are presented to illustrate its use for analysis of independent observations and data from clustered studies.
Keywords	Logistic model ; delta method ; bootstrap ; prevalence ratios ; Science ; Q
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	Academia Brasileira de Ciências
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Wavelet Support Vector Censored Regression

Mateus Maia / Jonatha Sousa Pimentel / Raydonal Ospina / Anderson Ara

Analytics, Vol 2, Iss 23, Pp 410-

2023 Volume 425

Abstract: Learning methods in survival analysis have the ability to handle censored observations. The Cox model is a predictive prevalent statistical technique for survival analysis, but its use rests on the strong assumption of hazard proportionality, which can ... ...

Abstract	Learning methods in survival analysis have the ability to handle censored observations. The Cox model is a predictive prevalent statistical technique for survival analysis, but its use rests on the strong assumption of hazard proportionality, which can be challenging to verify, particularly when working with non-linearity and high-dimensional data. Therefore, it may be necessary to consider a more flexible and generalizable approach, such as support vector machines. This paper aims to propose a new method, namely wavelet support vector censored regression, and compare the Cox model with traditional support vector regression and traditional support vector regression for censored data models, survival models based on support vector machines. In addition, to evaluate the effectiveness of different kernel functions in the support vector censored regression approach to survival data, we conducted a series of simulations with varying number of observations and ratios of censored data. Based on the simulation results, we found that the wavelet support vector censored regression outperformed the other methods in terms of the C-index. The evaluation was performed on simulations, survival benchmarking datasets and in a biomedical real application.
Keywords	survival analysis ; support vector censored regression ; support vector regression ; Cox ; Electronic computers. Computer science ; QA75.5-76.95 ; Probabilities. Mathematical statistics ; QA273-280
Subject code	310
Language	English
Publishing date	2023-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Weibull Regression and Machine Learning Survival Models

Thalytta Cavalcante / Raydonal Ospina / Víctor Leiva / Xavier Cabezas / Carlos Martin-Barreiro

Biology, Vol 12, Iss 442, p

Methodology, Comparison, and Application to Biomedical Data Related to Cardiac Surgery

2023 Volume 442

Abstract: In this article, we propose a comparative study between two models that can be used by researchers for the analysis of survival data: (i) the Weibull regression model and (ii) the random survival forest (RSF) model. The models are compared considering ... ...

Abstract	In this article, we propose a comparative study between two models that can be used by researchers for the analysis of survival data: (i) the Weibull regression model and (ii) the random survival forest (RSF) model. The models are compared considering the error rate, the performance of the model through the Harrell C-index, and the identification of the relevant variables for survival prediction. A statistical analysis of a data set from the Heart Institute of the University of São Paulo, Brazil, has been carried out. In the study, the length of stay of patients undergoing cardiac surgery, within the operating room, was used as the response variable. The obtained results show that the RSF model has less error rate for the training and testing data sets, at 23.55% and 20.31%, respectively, than the Weibull model, which has an error rate of 23.82%. Regarding the Harrell C-index, we obtain the values 0.76, 0.79, and 0.76, for the RSF and Weibull models, respectively. After the selection procedure, the Weibull model contains variables associated with the type of protocol and type of patient being statistically significant at 5%. The RSF model chooses age, type of patient, and type of protocol as relevant variables for prediction. We employ the randomForestSRC package of the R software to perform our data analysis and computational experiments. The proposal that we present has many applications in biology and medicine, which are discussed in the conclusions of this work.
Keywords	binary trees ; Harrell index ; model diagnostics ; non-normal regression ; random forest ; statistical software ; Biology (General) ; QH301-705.5
Subject code	310
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Adaptive kernel fuzzy clustering for missing data

Anny K. G. Rodrigues / Raydonal Ospina / Marcelo R. P. Ferreira

PLoS ONE, Vol 16, Iss

2021 Volume 11

Abstract: Many machine learning procedures, including clustering analysis are often affected by missing values. This work aims to propose and evaluate a Kernel Fuzzy C-means clustering algorithm considering the kernelization of the metric with local adaptive ... ...

Abstract	Many machine learning procedures, including clustering analysis are often affected by missing values. This work aims to propose and evaluate a Kernel Fuzzy C-means clustering algorithm considering the kernelization of the metric with local adaptive distances (VKFCM-K-LP) under three types of strategies to deal with missing data. The first strategy, called Whole Data Strategy (WDS), performs clustering only on the complete part of the dataset, i.e. it discards all instances with missing data. The second approach uses the Partial Distance Strategy (PDS), in which partial distances are computed among all available resources and then re-scaled by the reciprocal of the proportion of observed values. The third technique, called Optimal Completion Strategy (OCS), computes missing values iteratively as auxiliary variables in the optimization of a suitable objective function. The clustering results were evaluated according to different metrics. The best performance of the clustering algorithm was achieved under the PDS and OCS strategies. Under the OCS approach, new datasets were derive and the missing values were estimated dynamically in the optimization process. The results of clustering under the OCS strategy also presented a superior performance when compared to the resulting clusters obtained by applying the VKFCM-K-LP algorithm on a version where missing values are previously imputed by the mean or the median of the observed values.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Adaptive kernel fuzzy clustering for missing data.

Anny K G Rodrigues / Raydonal Ospina / Marcelo R P Ferreira

PLoS ONE, Vol 16, Iss 11, p e

2021 Volume 0259266

Abstract	Many machine learning procedures, including clustering analysis are often affected by missing values. This work aims to propose and evaluate a Kernel Fuzzy C-means clustering algorithm considering the kernelization of the metric with local adaptive distances (VKFCM-K-LP) under three types of strategies to deal with missing data. The first strategy, called Whole Data Strategy (WDS), performs clustering only on the complete part of the dataset, i.e. it discards all instances with missing data. The second approach uses the Partial Distance Strategy (PDS), in which partial distances are computed among all available resources and then re-scaled by the reciprocal of the proportion of observed values. The third technique, called Optimal Completion Strategy (OCS), computes missing values iteratively as auxiliary variables in the optimization of a suitable objective function. The clustering results were evaluated according to different metrics. The best performance of the clustering algorithm was achieved under the PDS and OCS strategies. Under the OCS approach, new datasets were derive and the missing values were estimated dynamically in the optimization process. The results of clustering under the OCS strategy also presented a superior performance when compared to the resulting clusters obtained by applying the VKFCM-K-LP algorithm on a version where missing values are previously imputed by the mean or the median of the observed values.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Statistical Properties of an Unassisted Image Quality Index for SAR Imagery

Luis Gomez / Raydonal Ospina / Alejandro C. Frery

Remote Sensing, Vol 11, Iss 4, p

2019 Volume 385

Abstract: ... The ... ... ... M ... ... ... estimator is a recently proposed image-quality index used to evaluate the despeckling operation in SAR (Synthetic Aperture Radar) data. It is used also to rank ... ...

Abstract	The <math display="inline"> <semantics> <mi mathvariant="script">M</mi> </semantics> </math> estimator is a recently proposed image-quality index used to evaluate the despeckling operation in SAR (Synthetic Aperture Radar) data. It is used also to rank despeckling filters and to improve their design. As a difference with traditional image-quality estimators, it operates not on the filtered result but on a derived one, i.e., the ratio image. However, a deep statistical analysis of its properties remains open and, with it, the ability to use it as a test statistic. In this work, we focus on obtaining insights into its distribution as well as on exploring other remarkable statistical properties of this unassisted estimator. This study is performed through EDA (Exploratory Data Analysis) and the well-known ANOVA (ANalysis Of VAriance). We test our results on a set of simulated SAR data and provide guides to enrich the <math display="inline"> <semantics> <mi mathvariant="script">M</mi> </semantics> </math> estimator to extend its capabilities.
Keywords	speckle ; SAR ; despeckling ; image-quality index ; <named-content content-type="equation"> <mml:math id="mm500" display="block"><mml:semantics><mml:mi mathvariant="script">M</mml:mi></mml:semantics></mml:math> </named-content> estimator ; ANOVA ; Science ; Q
Subject code	310
Language	English
Publishing date	2019-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Power law behaviour in the saturation regime of fatality curves of the COVID-19 pandemic

Giovani L. Vasconcelos / Antônio M. S. Macêdo / Gerson C. Duarte-Filho / Arthur A. Brum / Raydonal Ospina / Francisco A. G. Almeida

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 12

Abstract: Abstract We apply a versatile growth model, whose growth rate is given by a generalised beta distribution, to describe the complex behaviour of the fatality curves of the COVID-19 disease for several countries in Europe and North America. We show that ... ...

Abstract	Abstract We apply a versatile growth model, whose growth rate is given by a generalised beta distribution, to describe the complex behaviour of the fatality curves of the COVID-19 disease for several countries in Europe and North America. We show that the COVID-19 epidemic curves not only may present a subexponential early growth but can also exhibit a similar subexponential (power-law) behaviour in the saturation regime. We argue that the power-law exponent of the latter regime, which measures how quickly the curve approaches the plateau, is directly related to control measures, in the sense that the less strict the control, the smaller the exponent and hence the slower the diseases progresses to its end. The power-law saturation uncovered here is an important result, because it signals to policymakers and health authorities that it is important to keep control measures for as long as possible, so as to avoid a slow, power-law ending of the disease. The slower the approach to the plateau, the longer the virus lingers on in the population, and the greater not only the final death toll but also the risk of a resurgence of infections.
Keywords	Medicine ; R ; Science ; Q
Subject code	612
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers.

Osvaldo A Rosso / Raydonal Ospina / Alejandro C Frery

PLoS ONE, Vol 11, Iss 12, p e

2016 Volume 0166868

Abstract: We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated ...

Abstract	We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results are better than state-of-the-art online techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2016-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Visualization of Skewed Data

RAYDONAL OSPINA / ANTONIO MARCOS LARANGEIRAS / ALEJANDRO C. FRERY

Revista Colombiana de Estadística, Vol 37, Iss 2, Pp 399-

A Tool in R

2014 Volume 417

Abstract: After discussing the main characteristics of the histogram and of a number of variations in the boxplot, this work presents a visualization tool specifically tailored to deal with skewed data. The idea is to use various types of boxplots (the classical ... ...

Abstract	After discussing the main characteristics of the histogram and of a number of variations in the boxplot, this work presents a visualization tool specifically tailored to deal with skewed data. The idea is to use various types of boxplots (the classical one, which is tuned for skewness of the data, the shifting boxplot, and the box-percentile plot), the violin plot, and the histogram with a nonparametric estimate of the density overlay. The plots are presented in such a way that they facilitate the extraction of additional information from each one. We show that a good deal of information can be extracted from the inspection of the output using example data from synthetic aperture radar images. We provide an implementation in R based on functions already available.
Keywords	análisis exploratorio de datos ; boxplot ; datos sesgados gráficos de violin ; visualización ; Statistics ; HA1-4737 ; Social Sciences ; H
Language	Spanish
Publishing date	2014-12-01T00:00:00Z
Publisher	Universidad Nacional de Colombia
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Choosing the right strategy to model longitudinal count data in Epidemiology

Daniele de Brito Trindade / Raydonal Ospina / Leila D. Amorim

Epidemiology Biostatistics and Public Health, Vol 12, Iss

An application with CD4 cell counts

2015 Volume 4

Abstract: Background: Statistical models for analysis of correlated count data are important for answering epidemiological questions that involve taking individual count measurements repeatedly over time through the use of longitudinal studies. Conventional ... ...

Abstract	Background: Statistical models for analysis of correlated count data are important for answering epidemiological questions that involve taking individual count measurements repeatedly over time through the use of longitudinal studies. Conventional regression models for this type of data are inadequate, leading to improper conclusions and inference. An important application of longitudinal studies in Public Health is the evaluation and monitoring of patients with infectious diseases, such as HIV/AIDS, to determine their health status, to verify the treatment effects, and to make prognosis concerning the evolution of the disease, including interdependencies of clinical manifestations. The purpose of this article is to characterize different statistical strategies for analysis of longitudinal count data, emphasizing how to choose the most suitable model for the data and how to interpret the results. Methods: We illustrate their applicability by evaluating the effect of associated factors on lymphocyte CD4+T cell count in HIV seropositive patients in Salvador/Bahia - Brazil. We describe Poisson and Negative Binomial models using multilevel (ML) approach and generalized estimations equations (GEE) for analysis of longitudinal count data. Results: It is worth noting that the interpretation of the results from ML and GEE differs and they should not be compared directly. Conclusion: We believe that the statistical methodology for analysis of longitudinal studies with correlated count data can be useful to address several important questions in public health, particularly by helping to monitor patients and checking the effectiveness of treatments.
Keywords	Medicine (General) ; R5-920 ; Public aspects of medicine ; RA1-1270
Subject code	310
Language	English
Publishing date	2015-12-01T00:00:00Z
Publisher	Prex S.r.l.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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