The Sine Modified Lindley Distribution

The paper contributes majorly in the development of a flexible trigonometric extension of the well-known modified Lindley distribution. More precisely, we use features from the sine generalized family of distributions to create an original one-parameter survival distribution, called the sine modified Lindley distribution. As the main motivational fact, it provides an attractive alternative to the Lindley and modified Lindley distributions; it may be better able to model lifetime phenomena presenting data of leptokurtic nature. In the first part of the paper, we introduce it conceptually and discuss its key characteristics, such as functional, reliability, and moment analysis. Then, an applied study is conducted. The usefulness, applicability, and agility of the sine modified Lindley distribution are illustrated through a detailed study using simulation. Two real data sets from the engineering and climate sectors are analyzed. As a result, the sine modified Lindley model is proven to have a superior match to important models, such as the Lindley, modified Lindley, sine exponential, and sine Lindley models, based on goodness-of-fit criteria of importance.

On the Accuracy of the Sine Power Lomax Model for Data Fitting

Every day, new data must be analysed as well as possible in all areas of applied science, which requires the development of attractive statistical models, that is to say adapted to the context, easy to use and efficient. In this article, we innovate in this direction by proposing a new statistical model based on the functionalities of the sinusoidal transformation and power Lomax distribution. We thus introduce a new three-parameter survival distribution called sine power Lomax distribution. In a first approach, we present it theoretically and provide some of its significant properties. Then the practicality, utility and flexibility of the sine power Lomax model are demonstrated through a comprehensive simulation study, and the analysis of nine real datasets mainly from medicine and engineering. Based on relevant goodness of fit criteria, it is shown that the sine power Lomax model has a better fit to some of the existing Lomax-like distributions.

COVID-19 in Hospitalized Ethiopian Children: Characteristics and Outcome Profile

ABSTRACTBackgroundConsidering the number of people affected and the burden to the health care system due to the Coronavirus pandemic, there is still a gap in understanding the disease better leaving a space for new evidence to be filled by researchers. This scarcity of evidence is observed especially among children with the virus. Understanding the disease pattern and its effect among children is vital in providing timely and targeted intervention.AimTo assess the characteristics and outcome profile of 115 RT-PCR confirmed children with COVID-19, and to determine the presence of significant difference in disease severity and survival distribution between groups among children admitted to Millennium COVID-19 Care Center in Ethiopia.MethodsA prospective cohort study was conducted among 90 consecutively admitted eligible RT-PCR confirmed COVID-19 children from end of June to mid September, 2020. Frequency tables, KM plots, median survival times and Log-rank test were used to describe the data and compare survival distribution between groups. A chi-square test/ Fischer’s exact test were used to determine the presence of a significant difference between the independent variables and disease severity. A statistically significant difference was detected for variables with a P-value of ≤ 0.05. Survival experience of different groups was compared using KM survival curves. Log-rank test was used to assess the presence of significant difference among survival distributions of groups for equality where a statistically significant difference in survival distribution between groups was detected for variables with a P-value of ≤ 0.05.ResultsFrom the 90 children, 67 (74.4%) achieved clinical improvement and 23 (25.6%) were censored. There was no death. The median time to clinical improvement was 14 days. The median age of the participants was 15 years and 63.3% of the participants were females. The commonest reported route of disease transmission was through close contact with a diagnosed person (45.6%). Only three (3.3%) had a history of pre-existing comorbid illness. More than a quarter (26.7%) had one or more symptoms at admission, the commonest being cough (22.2%). Seventy three (81.1%) of the patients had mild COVID-19 at admission and the rest (18.9%) had moderate disease. On the chi-square and Fischer’s exact test, children with one or more symptom at presentation (73.3% Vs 36.7%, p-value= 0.0001), fever (40.0 % Vs 60.0%, p-value=0.045), cough (20.0 % Vs 80.0%, p-value=0.0001), sore throat (44.4 % Vs 55.6%, p-value=0.011), and headache (44.4 % Vs 55.6%, p-value=0.011) were more likely to develop moderate COVID-19. On the log rank test, a significant difference in survival between groups was observed only for sex. A significantly longer time was needed for female patients to achieve clinical improvement compared to male patients (15 days Vs 14 days, p-value= 0.042).ConclusionsThe average duration of time to clinical improvement was 14 days and 74.4% achieved clinical improvement. There was no death during the observation period. The pediatric patients seemed to have a milder disease presentation and a favorable outcome compared to other countries report and also the adult pattern observed in our country. Having particular symptom groups is associated with the development of moderate COVID-19. Being female seemed to delay the time to clinical improvement. Further multicenter study with a large sample size is recommended to reach at a better conclusion.

Confidence Interval for Change Point in Hazard Rate With Staggered Entry

We consider the construction of a con?dence region (interval) for a change point in hazard rate of the patients survival distribution when the patients enter the trial at random times. We show that the local- likelihood ratio process converges weakly to a certain process and obtain the maximum distribution of the process which does not depend on the change point, and thus can be used to construct the confidence region for the change point. We also compare the limiting density function to the empirical density and discuss the empirical coverage probability of the confidence interval by simulation. Stanford Heart Transplant data are used for illustration.

The long-term inverse Nakagami distribution: Properties, inference and application

In this paper, a new long-term survival distribution, the so-called long-term inverse Nakagami distribution, is presented. The proposed distribution allows us to fit data with unimodal hazard function, where a part of the population is not susceptible to the event of interest, the so-called long-term survival. This distribution can be used, for instance, in clinical studies where a portion of the population can be cured during a treatment. Some mathematical properties of the new distribution are derived. The inferential procedures for the parameters are discussed under the maximum likelihood estimators. A numerical simulation study is carried out to verify the performance of these estimators. Finally, an application to real data on patients’ lifetime after acute myocardial infarction illustrates the usefulness of the proposed distribution.