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  • 1
    Online Resource
    Online Resource
    Prevalence and factors associated with neonatal mortality among neonates hospitalized at the National Hospital Nouakchott, Mauritania
    Pan African Medical Journal ; 2019
    In:  Pan African Medical Journal Vol. 34 ( 2019-11-18)
    Details
    In: Pan African Medical Journal, Pan African Medical Journal, Vol. 34 ( 2019-11-18)
    Type of Medium: Online Resource
    ISSN: 1937-8688
    URL: Article
    DOI: 10.11604/pamj.2019.34.152.14683
    Language: English
    Publisher: Pan African Medical Journal
    Publication Date: 2019
    detail.hit.zdb_id: 2514347-5
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  • 2
    Online Resource
    Online Resource
    Estimation of the Average Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification
    MDPI AG ; 2021
    In:  Mathematics Vol. 9, No. 14 ( 2021-07-19), p. 1694-
    Details
    In: Mathematics, MDPI AG, Vol. 9, No. 14 ( 2021-07-19), p. 1694-
    Abstract: The average kappa coefficient of a binary diagnostic test is a measure of the beyond-chance average agreement between the binary diagnostic test and the gold standard, and it depends on the sensitivity and specificity of the diagnostic test and on disease prevalence. In this manuscript the estimation of the average kappa coefficient of a diagnostic test in the presence of verification bias is studied. Confidence intervals for the average kappa coefficient are studied applying the methods of maximum likelihood and multiple imputation by chained equations. Simulation experiments have been carried out to study the asymptotic behaviors of the proposed intervals, given some application rules. The results obtained in our simulation experiments have shown that the multiple imputation by chained equations method provides better results than the maximum likelihood method. A function has been written in R to estimate the average kappa coefficient by applying multiple imputation. The results have been applied to the diagnosis of liver disease.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math9141694
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704244-3
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  • 3
    Online Resource
    Online Resource
    Comparison of the Average Kappa Coefficients of Two Binary Diagnostic Tests with Missing Data
    MDPI AG ; 2021
    In:  Mathematics Vol. 9, No. 21 ( 2021-11-08), p. 2834-
    Details
    In: Mathematics, MDPI AG, Vol. 9, No. 21 ( 2021-11-08), p. 2834-
    Abstract: The average kappa coefficient of a binary diagnostic test is a parameter that measures the average beyond-chance agreement between the diagnostic test and the gold standard. This parameter depends on the accuracy of the diagnostic test and also on the disease prevalence. This article studies the comparison of the average kappa coefficients of two binary diagnostic tests when the gold standard is not applied to all individuals in a random sample. In this situation, known as partial disease verification, the disease status of some individuals is a missing piece of data. Assuming that the missing data mechanism is missing at random, the comparison of the average kappa coefficients is solved by applying two computational methods: the EM algorithm and the SEM algorithm. With the EM algorithm the parameters are estimated and with the SEM algorithm their variances-covariances are estimated. Simulation experiments have been carried out to study the sizes and powers of the hypothesis tests studied, obtaining that the proposed method has good asymptotic behavior. A function has been written in R to solve the proposed problem, and the results obtained have been applied to the diagnosis of Alzheimer's disease.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math9212834
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704244-3
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  • 4
    Online Resource
    Online Resource
    Global Hypothesis Test to Compare the Predictive Values of Diagnostic Tests Subject to a Case-Control Design
    MDPI AG ; 2021
    In:  Mathematics Vol. 9, No. 6 ( 2021-03-19), p. 658-
    Details
    In: Mathematics, MDPI AG, Vol. 9, No. 6 ( 2021-03-19), p. 658-
    Abstract: Use of a case-control design to compare the accuracy of two binary diagnostic tests is frequent in clinical practice. This design consists of applying the two diagnostic tests to all of the individuals in a sample of those who have the disease and in another sample of those who do not have the disease. This manuscript studies the comparison of the predictive values of two diagnostic tests subject to a case-control design. A global hypothesis test, based on the chi-square distribution, is proposed to compare the predictive values simultaneously, as well as other alternative methods. The hypothesis tests studied require knowing the prevalence of the disease. Simulation experiments were carried out to study the type I errors and the powers of the hypothesis tests proposed, as well as to study the effect of a misspecification of the prevalence on the asymptotic behavior of the hypothesis tests and on the estimators of the predictive values. The proposed global hypothesis test was extended to the situation in which there are more than two diagnostic tests. The results have been applied to the diagnosis of coronary disease.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math9060658
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704244-3
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  • 5
    Online Resource
    Online Resource
    Confidence Intervals and Sample Size to Compare the Predictive Values of Two Diagnostic Tests
    MDPI AG ; 2021
    In:  Mathematics Vol. 9, No. 13 ( 2021-06-22), p. 1462-
    Details
    In: Mathematics, MDPI AG, Vol. 9, No. 13 ( 2021-06-22), p. 1462-
    Abstract: A binary diagnostic test is a medical test that is applied to an individual in order to determine the presence or the absence of a certain disease and whose result can be positive or negative. A positive result indicates the presence of the disease, and a negative result indicates the absence. Positive and negative predictive values represent the accuracy of a binary diagnostic test when it is applied to a cohort of individuals, and they are measures of the clinical accuracy of the binary diagnostic test. In this manuscript, we study the comparison of the positive (negative) predictive values of two binary diagnostic tests subject to a paired design through confidence intervals. We have studied confidence intervals for the difference and for the ratio of the two positive (negative) predictive values. Simulation experiments have been carried out to study the asymptotic behavior of the confidence intervals, giving some general rules for application. We also study a method to calculate the sample size to compare the parameters using confidence intervals. We have written a program in R to solve the problems studied in this manuscript. The results have been applied to the diagnosis of colorectal cancer.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math9131462
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704244-3
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